---
_id: '48275'
abstract:
- lang: eng
text: "MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription
systems.\r\nIt provides a unified interface for the computation of commonly used
Word Error Rates (WERs), specifically cpWER, ORC WER and MIMO WER along other
WER definitions.\r\nWe extend the cpWER computation by a temporal constraint to
ensure that only words are identified as correct when the temporal alignment is
plausible.\r\nThis leads to a better quality of the matching of the hypothesis
string to the reference string that more closely resembles the actual transcription
quality, and a system is penalized if it provides poor time annotations.\r\nSince
word-level timing information is often not available, we present a way to approximate
exact word-level timings from segment-level timings (e.g., a sentence) and show
that the approximation leads to a similar WER as a matching with exact word-level
annotations.\r\nAt the same time, the time constraint leads to a speedup of the
matching algorithm, which outweighs the additional overhead caused by processing
the time stamps."
author:
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'von Neumann T, Boeddeker C, Delcroix M, Haeb-Umbach R. MeetEval: A Toolkit
for Computation of Word Error Rates for Meeting Transcription Systems. In: Proc.
CHiME 2023 Workshop on Speech Processing in Everyday Environments. ; 2023.'
apa: 'von Neumann, T., Boeddeker, C., Delcroix, M., & Haeb-Umbach, R. (2023).
MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription
Systems. Proc. CHiME 2023 Workshop on Speech Processing in Everyday Environments.
CHiME 2023 Workshop on Speech Processing in Everyday Environments, Dublin.'
bibtex: '@inproceedings{von Neumann_Boeddeker_Delcroix_Haeb-Umbach_2023, title={MeetEval:
A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems},
booktitle={Proc. CHiME 2023 Workshop on Speech Processing in Everyday Environments},
author={von Neumann, Thilo and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach,
Reinhold}, year={2023} }'
chicago: 'Neumann, Thilo von, Christoph Boeddeker, Marc Delcroix, and Reinhold Haeb-Umbach.
“MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription
Systems.” In Proc. CHiME 2023 Workshop on Speech Processing in Everyday Environments,
2023.'
ieee: 'T. von Neumann, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach, “MeetEval:
A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems,”
presented at the CHiME 2023 Workshop on Speech Processing in Everyday Environments,
Dublin, 2023.'
mla: 'von Neumann, Thilo, et al. “MeetEval: A Toolkit for Computation of Word Error
Rates for Meeting Transcription Systems.” Proc. CHiME 2023 Workshop on Speech
Processing in Everyday Environments, 2023.'
short: 'T. von Neumann, C. Boeddeker, M. Delcroix, R. Haeb-Umbach, in: Proc. CHiME
2023 Workshop on Speech Processing in Everyday Environments, 2023.'
conference:
location: Dublin
name: CHiME 2023 Workshop on Speech Processing in Everyday Environments
date_created: 2023-10-19T07:24:51Z
date_updated: 2025-02-12T09:12:05Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: tvn
date_created: 2023-10-19T07:19:59Z
date_updated: 2023-10-19T07:19:59Z
file_id: '48276'
file_name: Chime_7__MeetEval.pdf
file_size: 263744
relation: main_file
file_date_updated: 2023-10-19T07:19:59Z
has_accepted_license: '1'
keyword:
- Speech Recognition
- Word Error Rate
- Meeting Transcription
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2307.11394
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
- _id: '508'
grant_number: '448568305'
name: Automatische Transkription von Gesprächssituationen
publication: Proc. CHiME 2023 Workshop on Speech Processing in Everyday Environments
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/fgnt/meeteval
status: public
title: 'MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription
Systems'
type: conference
user_id: '40767'
year: '2023'
...
---
_id: '26770'
abstract:
- lang: eng
text: "Automatic transcription of meetings requires handling of overlapped speech,
which calls for continuous speech separation (CSS) systems. The uPIT criterion
was proposed for utterance-level separation with neural networks and introduces
the constraint that the total number of speakers must not exceed the number of
output channels. When processing meeting-like data in a segment-wise manner, i.e.,
by separating overlapping segments independently and stitching adjacent segments
to continuous output streams, this constraint has to be fulfilled for any segment.
In this contribution, we show that this constraint can be significantly relaxed.
We propose a novel graph-based PIT criterion, which casts the assignment of utterances
to output channels in a graph coloring problem. It only requires that the number
of concurrently active speakers must not exceed the number of output channels.
As a consequence, the system can process an arbitrary number of speakers and arbitrarily
long segments and thus can handle more diverse scenarios.\r\nFurther, the stitching
algorithm for obtaining a consistent output order in neighboring segments is of
less importance and can even be eliminated completely, not the least reducing
the computational effort. Experiments on meeting-style WSJ data show improvements
in recognition performance over using the uPIT criterion. "
author:
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'von Neumann T, Kinoshita K, Boeddeker C, Delcroix M, Haeb-Umbach R. Graph-PIT:
Generalized Permutation Invariant Training for Continuous Separation of Arbitrary
Numbers of Speakers. In: Interspeech 2021. ; 2021. doi:10.21437/interspeech.2021-1177'
apa: 'von Neumann, T., Kinoshita, K., Boeddeker, C., Delcroix, M., & Haeb-Umbach,
R. (2021). Graph-PIT: Generalized Permutation Invariant Training for Continuous
Separation of Arbitrary Numbers of Speakers. Interspeech 2021. Interspeech.
https://doi.org/10.21437/interspeech.2021-1177'
bibtex: '@inproceedings{von Neumann_Kinoshita_Boeddeker_Delcroix_Haeb-Umbach_2021,
title={Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation
of Arbitrary Numbers of Speakers}, DOI={10.21437/interspeech.2021-1177},
booktitle={Interspeech 2021}, author={von Neumann, Thilo and Kinoshita, Keisuke
and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}, year={2021}
}'
chicago: 'Neumann, Thilo von, Keisuke Kinoshita, Christoph Boeddeker, Marc Delcroix,
and Reinhold Haeb-Umbach. “Graph-PIT: Generalized Permutation Invariant Training
for Continuous Separation of Arbitrary Numbers of Speakers.” In Interspeech
2021, 2021. https://doi.org/10.21437/interspeech.2021-1177.'
ieee: 'T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach,
“Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation
of Arbitrary Numbers of Speakers,” presented at the Interspeech, 2021, doi: 10.21437/interspeech.2021-1177.'
mla: 'von Neumann, Thilo, et al. “Graph-PIT: Generalized Permutation Invariant Training
for Continuous Separation of Arbitrary Numbers of Speakers.” Interspeech 2021,
2021, doi:10.21437/interspeech.2021-1177.'
short: 'T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, R. Haeb-Umbach,
in: Interspeech 2021, 2021.'
conference:
name: Interspeech
date_created: 2021-10-25T08:50:01Z
date_updated: 2023-11-15T12:14:40Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.21437/interspeech.2021-1177
file:
- access_level: open_access
content_type: video/mp4
creator: tvn
date_created: 2021-12-06T10:39:13Z
date_updated: 2021-12-06T10:48:30Z
file_id: '28327'
file_name: Interspeech 2021 voiceover-002-compressed.mp4
file_size: 9550220
relation: supplementary_material
title: Video for INTERSPEECH 2021
- access_level: open_access
content_type: application/vnd.openxmlformats-officedocument.presentationml.presentation
creator: tvn
date_created: 2021-12-06T10:47:01Z
date_updated: 2021-12-06T10:47:01Z
file_id: '28328'
file_name: Graph-PIT-poster-presentation.pptx
file_size: 1337297
relation: slides
title: Slides from INTERSPEECH 2021
- access_level: open_access
content_type: application/pdf
creator: tvn
date_created: 2021-12-06T10:48:21Z
date_updated: 2021-12-06T10:48:21Z
file_id: '28329'
file_name: INTERSPEECH2021_Graph_PIT.pdf
file_size: 226589
relation: main_file
file_date_updated: 2021-12-06T10:48:30Z
has_accepted_license: '1'
keyword:
- Continuous speech separation
- automatic speech recognition
- overlapped speech
- permutation invariant training
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Interspeech 2021
publication_status: published
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/fgnt/graph_pit
status: public
title: 'Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation
of Arbitrary Numbers of Speakers'
type: conference
user_id: '49870'
year: '2021'
...
---
_id: '11813'
abstract:
- lang: eng
text: 'The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising
Autoencoder (DA) both bring performance gains in severe single-channel speech
recognition conditions. The first can be adjusted to different conditions by an
appropriate parameter setting, while the latter needs to be trained on conditions
similar to the ones expected at decoding time, making it vulnerable to a mismatch
between training and test conditions. We use a DNN backend and study reverberant
ASR under three types of mismatch conditions: different room reverberation times,
different speaker to microphone distances and the difference between artificially
reverberated data and the recordings in a reverberant environment. We show that
for these mismatch conditions BFE can provide the targets for a DA. This unsupervised
adaptation provides a performance gain over the direct use of BFE and even enables
to compensate for the mismatch of real and simulated reverberant data.'
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: P.
full_name: Golik, P.
last_name: Golik
- first_name: R.
full_name: Schlueter, R.
last_name: Schlueter
citation:
ama: 'Heymann J, Haeb-Umbach R, Golik P, Schlueter R. Unsupervised adaptation of
a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under
mismatch conditions. In: Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference On. ; 2015:5053-5057. doi:10.1109/ICASSP.2015.7178933'
apa: Heymann, J., Haeb-Umbach, R., Golik, P., & Schlueter, R. (2015). Unsupervised
adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
asr under mismatch conditions. In Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference on (pp. 5053–5057). https://doi.org/10.1109/ICASSP.2015.7178933
bibtex: '@inproceedings{Heymann_Haeb-Umbach_Golik_Schlueter_2015, title={Unsupervised
adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
asr under mismatch conditions}, DOI={10.1109/ICASSP.2015.7178933},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
Conference on}, author={Heymann, Jahn and Haeb-Umbach, Reinhold and Golik, P.
and Schlueter, R.}, year={2015}, pages={5053–5057} }'
chicago: Heymann, Jahn, Reinhold Haeb-Umbach, P. Golik, and R. Schlueter. “Unsupervised
Adaptation of a Denoising Autoencoder by Bayesian Feature Enhancement for Reverberant
Asr under Mismatch Conditions.” In Acoustics, Speech and Signal Processing
(ICASSP), 2015 IEEE International Conference On, 5053–57, 2015. https://doi.org/10.1109/ICASSP.2015.7178933.
ieee: J. Heymann, R. Haeb-Umbach, P. Golik, and R. Schlueter, “Unsupervised adaptation
of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr
under mismatch conditions,” in Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference on, 2015, pp. 5053–5057.
mla: Heymann, Jahn, et al. “Unsupervised Adaptation of a Denoising Autoencoder by
Bayesian Feature Enhancement for Reverberant Asr under Mismatch Conditions.” Acoustics,
Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On,
2015, pp. 5053–57, doi:10.1109/ICASSP.2015.7178933.
short: 'J. Heymann, R. Haeb-Umbach, P. Golik, R. Schlueter, in: Acoustics, Speech
and Signal Processing (ICASSP), 2015 IEEE International Conference On, 2015, pp.
5053–5057.'
date_created: 2019-07-12T05:28:45Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2015.7178933
keyword:
- codecs
- signal denoising
- speech recognition
- Bayesian feature enhancement
- denoising autoencoder
- reverberant ASR
- single-channel speech recognition
- speaker to microphone distances
- unsupervised adaptation
- Adaptation models
- Noise reduction
- Reverberation
- Speech
- Speech recognition
- Training
- deep neuronal networks
- denoising autoencoder
- feature enhancement
- robust speech recognition
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/hey_icassp_2015.pdf
oa: '1'
page: 5053-5057
publication: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
Conference on
status: public
title: Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement
for reverberant asr under mismatch conditions
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '11861'
abstract:
- lang: eng
text: 'In this contribution we present a theoretical and experimental investigation
into the effects of reverberation and noise on features in the logarithmic mel
power spectral domain, an intermediate stage in the computation of the mel frequency
cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining
insight into the complex interaction between clean speech, noise, and noisy reverberant
speech features is essential for any ASR system to be robust against noise and
reverberation present in distant microphone input signals. The findings are gathered
in a probabilistic formulation of an observation model which may be used in model-based
feature compensation schemes. The proposed observation model extends previous
models in three major directions: First, the contribution of additive background
noise to the observation error is explicitly taken into account. Second, an energy
compensation constant is introduced which ensures an unbiased estimate of the
reverberant speech features, and, third, a recursive variant of the observation
model is developed resulting in reduced computational complexity when used in
model-based feature compensation. The experimental section is used to evaluate
the accuracy of the model and to describe how its parameters can be determined
from test data.'
author:
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Leutnant V, Krueger A, Haeb-Umbach R. A New Observation Model in the Logarithmic
Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2014;22(1):95-109.
doi:10.1109/TASLP.2013.2285480
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2014). A New Observation
Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition
of Noisy Reverberant Speech. IEEE/ACM Transactions on Audio, Speech, and Language
Processing, 22(1), 95–109. https://doi.org/10.1109/TASLP.2013.2285480
bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2014, title={A New Observation Model
in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of
Noisy Reverberant Speech}, volume={22}, DOI={10.1109/TASLP.2013.2285480},
number={1}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2014},
pages={95–109} }'
chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A New
Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic
Recognition of Noisy Reverberant Speech.” IEEE/ACM Transactions on Audio, Speech,
and Language Processing 22, no. 1 (2014): 95–109. https://doi.org/10.1109/TASLP.2013.2285480.'
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A New Observation Model in the
Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant
Speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing,
vol. 22, no. 1, pp. 95–109, 2014.
mla: Leutnant, Volker, et al. “A New Observation Model in the Logarithmic Mel Power
Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” IEEE/ACM
Transactions on Audio, Speech, and Language Processing, vol. 22, no. 1, 2014,
pp. 95–109, doi:10.1109/TASLP.2013.2285480.
short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE/ACM Transactions on Audio,
Speech, and Language Processing 22 (2014) 95–109.
date_created: 2019-07-12T05:29:41Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASLP.2013.2285480
intvolume: ' 22'
issue: '1'
keyword:
- computational complexity
- reverberation
- speech recognition
- automatic speech recognition
- background noise
- clean speech
- computational complexity
- energy compensation
- logarithmic mel power spectral domain
- mel frequency cepstral coefficients
- microphone input signals
- model-based feature compensation schemes
- noisy reverberant speech automatic recognition
- noisy reverberant speech features
- reverberation
- Atmospheric modeling
- Computational modeling
- Noise
- Noise measurement
- Reverberation
- Speech
- Vectors
- Model-based feature compensation
- observation model for reverberant and noisy speech
- recursive observation model
- robust automatic speech recognition
language:
- iso: eng
page: 95-109
publication: IEEE/ACM Transactions on Audio, Speech, and Language Processing
publication_identifier:
issn:
- 2329-9290
status: public
title: A New Observation Model in the Logarithmic Mel Power Spectral Domain for the
Automatic Recognition of Noisy Reverberant Speech
type: journal_article
user_id: '44006'
volume: 22
year: '2014'
...
---
_id: '11867'
abstract:
- lang: eng
text: 'New waves of consumer-centric applications, such as voice search and voice
interaction with mobile devices and home entertainment systems, increasingly require
automatic speech recognition (ASR) to be robust to the full range of real-world
noise and other acoustic distorting conditions. Despite its practical importance,
however, the inherent links between and distinctions among the myriad of methods
for noise-robust ASR have yet to be carefully studied in order to advance the
field further. To this end, it is critical to establish a solid, consistent, and
common mathematical foundation for noise-robust ASR, which is lacking at present.
This article is intended to fill this gap and to provide a thorough overview of
modern noise-robust techniques for ASR developed over the past 30 years. We emphasize
methods that are proven to be successful and that are likely to sustain or expand
their future applicability. We distill key insights from our comprehensive overview
in this field and take a fresh look at a few old problems, which nevertheless
are still highly relevant today. Specifically, we have analyzed and categorized
a wide range of noise-robust techniques using five different criteria: 1) feature-domain
vs. model-domain processing, 2) the use of prior knowledge about the acoustic
environment distortion, 3) the use of explicit environment-distortion models,
4) deterministic vs. uncertainty processing, and 5) the use of acoustic models
trained jointly with the same feature enhancement or model adaptation process
used in the testing stage. With this taxonomy-oriented review, we equip the reader
with the insight to choose among techniques and with the awareness of the performance-complexity
tradeoffs. The pros and cons of using different noise-robust ASR techniques in
practical application scenarios are provided as a guide to interested practitioners.
The current challenges and future research directions in this field is also carefully
analyzed.'
author:
- first_name: Jinyu
full_name: Li, Jinyu
last_name: Li
- first_name: Li
full_name: Deng, Li
last_name: Deng
- first_name: Yifan
full_name: Gong, Yifan
last_name: Gong
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Li J, Deng L, Gong Y, Haeb-Umbach R. An Overview of Noise-Robust Automatic
Speech Recognition. IEEE Transactions on Audio, Speech and Language Processing.
2014;22(4):745-777. doi:10.1109/TASLP.2014.2304637
apa: Li, J., Deng, L., Gong, Y., & Haeb-Umbach, R. (2014). An Overview of Noise-Robust
Automatic Speech Recognition. IEEE Transactions on Audio, Speech and Language
Processing, 22(4), 745–777. https://doi.org/10.1109/TASLP.2014.2304637
bibtex: '@article{Li_Deng_Gong_Haeb-Umbach_2014, title={An Overview of Noise-Robust
Automatic Speech Recognition}, volume={22}, DOI={10.1109/TASLP.2014.2304637},
number={4}, journal={IEEE Transactions on Audio, Speech and Language Processing},
author={Li, Jinyu and Deng, Li and Gong, Yifan and Haeb-Umbach, Reinhold}, year={2014},
pages={745–777} }'
chicago: 'Li, Jinyu, Li Deng, Yifan Gong, and Reinhold Haeb-Umbach. “An Overview
of Noise-Robust Automatic Speech Recognition.” IEEE Transactions on Audio,
Speech and Language Processing 22, no. 4 (2014): 745–77. https://doi.org/10.1109/TASLP.2014.2304637.'
ieee: J. Li, L. Deng, Y. Gong, and R. Haeb-Umbach, “An Overview of Noise-Robust
Automatic Speech Recognition,” IEEE Transactions on Audio, Speech and Language
Processing, vol. 22, no. 4, pp. 745–777, 2014.
mla: Li, Jinyu, et al. “An Overview of Noise-Robust Automatic Speech Recognition.”
IEEE Transactions on Audio, Speech and Language Processing, vol. 22, no.
4, 2014, pp. 745–77, doi:10.1109/TASLP.2014.2304637.
short: J. Li, L. Deng, Y. Gong, R. Haeb-Umbach, IEEE Transactions on Audio, Speech
and Language Processing 22 (2014) 745–777.
date_created: 2019-07-12T05:29:47Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASLP.2014.2304637
intvolume: ' 22'
issue: '4'
keyword:
- Speech recognition
- compensation
- distortion modeling
- joint model training
- noise
- robustness
- uncertainty processing
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6732927
oa: '1'
page: 745-777
publication: IEEE Transactions on Audio, Speech and Language Processing
status: public
title: An Overview of Noise-Robust Automatic Speech Recognition
type: journal_article
user_id: '44006'
volume: 22
year: '2014'
...
---
_id: '11716'
abstract:
- lang: eng
text: The accuracy of automatic speech recognition systems in noisy and reverberant
environments can be improved notably by exploiting the uncertainty of the estimated
speech features using so-called uncertainty-of-observation techniques. In this
paper, we introduce a new Bayesian decision rule that can serve as a mathematical
framework from which both known and new uncertainty-of-observation techniques
can be either derived or approximated. The new decision rule in its direct form
leads to the new significance decoding approach for Gaussian mixture models, which
results in better performance compared to standard uncertainty-of-observation
techniques in different additive and convolutive noise scenarios.
author:
- first_name: Ahmed H.
full_name: Abdelaziz, Ahmed H.
last_name: Abdelaziz
- first_name: Steffen
full_name: Zeiler, Steffen
last_name: Zeiler
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance
decoding. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference On. ; 2013:6827-6831. doi:10.1109/ICASSP.2013.6638984'
apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., & Haeb-Umbach,
R. (2013). GMM-based significance decoding. In Acoustics, Speech and Signal
Processing (ICASSP), 2013 IEEE International Conference on (pp. 6827–6831).
https://doi.org/10.1109/ICASSP.2013.6638984
bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based
significance decoding}, DOI={10.1109/ICASSP.2013.6638984},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea
and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831}
}'
chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant,
and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
6827–31, 2013. https://doi.org/10.1109/ICASSP.2013.6638984.
ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based
significance decoding,” in Acoustics, Speech and Signal Processing (ICASSP),
2013 IEEE International Conference on, 2013, pp. 6827–6831.
mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
2013, pp. 6827–31, doi:10.1109/ICASSP.2013.6638984.
short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in:
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference
On, 2013, pp. 6827–6831.'
date_created: 2019-07-12T05:26:53Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638984
keyword:
- Bayes methods
- Gaussian processes
- convolution
- decision theory
- decoding
- noise
- reverberation
- speech coding
- speech recognition
- Bayesian decision rule
- GMM
- Gaussian mixture models
- additive noise scenarios
- automatic speech recognition systems
- convolutive noise scenarios
- decoding approach
- mathematical framework
- reverberant environments
- significance decoding
- speech feature estimation
- uncertainty-of-observation techniques
- Hidden Markov models
- Maximum likelihood decoding
- Noise
- Speech
- Speech recognition
- Uncertainty
- Uncertainty-of-observation
- modified imputation
- noise robust speech recognition
- significance decoding
- uncertainty decoding
language:
- iso: eng
page: 6827-6831
publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on
publication_identifier:
issn:
- 1520-6149
status: public
title: GMM-based significance decoding
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11862'
abstract:
- lang: eng
text: In this contribution we extend a previously proposed Bayesian approach for
the enhancement of reverberant logarithmic mel power spectral coefficients for
robust automatic speech recognition to the additional compensation of background
noise. A recently proposed observation model is employed whose time-variant observation
error statistics are obtained as a side product of the inference of the a posteriori
probability density function of the clean speech feature vectors. Further a reduction
of the computational effort and the memory requirements are achieved by using
a recursive formulation of the observation model. The performance of the proposed
algorithms is first experimentally studied on a connected digits recognition task
with artificially created noisy reverberant data. It is shown that the use of
the time-variant observation error model leads to a significant error rate reduction
at low signal-to-noise ratios compared to a time-invariant model. Further experiments
were conducted on a 5000 word task recorded in a reverberant and noisy environment.
A significant word error rate reduction was obtained demonstrating the effectiveness
of the approach on real-world data.
author:
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Leutnant V, Krueger A, Haeb-Umbach R. Bayesian Feature Enhancement for Reverberation
and Noise Robust Speech Recognition. IEEE Transactions on Audio, Speech, and
Language Processing. 2013;21(8):1640-1652. doi:10.1109/TASL.2013.2258013
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2013). Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition. IEEE Transactions on
Audio, Speech, and Language Processing, 21(8), 1640–1652. https://doi.org/10.1109/TASL.2013.2258013
bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={10.1109/TASL.2013.2258013},
number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2013},
pages={1640–1652} }'
chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian
Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” IEEE
Transactions on Audio, Speech, and Language Processing 21, no. 8 (2013): 1640–52.
https://doi.org/10.1109/TASL.2013.2258013.'
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition,” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 21, no. 8, pp. 1640–1652, 2013.
mla: Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and
Noise Robust Speech Recognition.” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 21, no. 8, 2013, pp. 1640–52, doi:10.1109/TASL.2013.2258013.
short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
and Language Processing 21 (2013) 1640–1652.
date_created: 2019-07-12T05:29:42Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2013.2258013
intvolume: ' 21'
issue: '8'
keyword:
- Bayes methods
- compensation
- error statistics
- reverberation
- speech recognition
- Bayesian feature enhancement
- background noise
- clean speech feature vectors
- compensation
- connected digits recognition task
- error statistics
- memory requirements
- noisy reverberant data
- posteriori probability density function
- recursive formulation
- reverberant logarithmic mel power spectral coefficients
- robust automatic speech recognition
- signal-to-noise ratios
- time-variant observation
- word error rate reduction
- Robust automatic speech recognition
- model-based Bayesian feature enhancement
- observation model for reverberant and noisy speech
- recursive observation model
language:
- iso: eng
page: 1640-1652
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 21
year: '2013'
...
---
_id: '11864'
abstract:
- lang: eng
text: In this work, an observation model for the joint compensation of noise and
reverberation in the logarithmic mel power spectral density domain is considered.
It relates the features of the noisy reverberant speech to those of the non-reverberant
speech and the noise. In contrast to enhancement of features only corrupted by
reverberation (reverberant features), enhancement of noisy reverberant features
requires a more sophisticated model for the error introduced by the proposed observation
model. In a first consideration, it will be shown that this error is highly dependent
on the instantaneous ratio of the power of reverberant speech to the power of
the noise and, moreover, sensitive to the phase between reverberant speech and
noise in the short-time discrete Fourier domain. Afterwards, a statistically motivated
approach will be presented allowing for the model of the observation error to
be inferred from the error model previously used for the reverberation only case.
Finally, the developed observation error model will be utilized in a Bayesian
feature enhancement scheme, leading to improvements in word accuracy on the AURORA5
database.
author:
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Leutnant V, Krueger A, Haeb-Umbach R. A Statistical Observation Model For
Noisy Reverberant Speech Features and its Application to Robust ASR. In: Signal
Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference
On. ; 2012.'
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2012). A Statistical Observation
Model For Noisy Reverberant Speech Features and its Application to Robust ASR.
In Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International
Conference on.
bibtex: '@inproceedings{Leutnant_Krueger_Haeb-Umbach_2012, title={A Statistical
Observation Model For Noisy Reverberant Speech Features and its Application to
Robust ASR}, booktitle={Signal Processing, Communications and Computing (ICSPCC),
2012 IEEE International Conference on}, author={Leutnant, Volker and Krueger,
Alexander and Haeb-Umbach, Reinhold}, year={2012} }'
chicago: Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A Statistical
Observation Model For Noisy Reverberant Speech Features and Its Application to
Robust ASR.” In Signal Processing, Communications and Computing (ICSPCC), 2012
IEEE International Conference On, 2012.
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A Statistical Observation Model
For Noisy Reverberant Speech Features and its Application to Robust ASR,” in Signal
Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference
on, 2012.
mla: Leutnant, Volker, et al. “A Statistical Observation Model For Noisy Reverberant
Speech Features and Its Application to Robust ASR.” Signal Processing, Communications
and Computing (ICSPCC), 2012 IEEE International Conference On, 2012.
short: 'V. Leutnant, A. Krueger, R. Haeb-Umbach, in: Signal Processing, Communications
and Computing (ICSPCC), 2012 IEEE International Conference On, 2012.'
date_created: 2019-07-12T05:29:44Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
keyword:
- Robust Automatic Speech Recognition
- Bayesian feature enhancement
- observation model for reverberant and noisy speech
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6335731
oa: '1'
publication: Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International
Conference on
status: public
title: A Statistical Observation Model For Noisy Reverberant Speech Features and its
Application to Robust ASR
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11846'
abstract:
- lang: eng
text: In this paper, we present a new technique for automatic speech recognition
(ASR) in reverberant environments. Our approach is aimed at the enhancement of
the logarithmic Mel power spectrum, which is computed at an intermediate stage
to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the
reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean
square error estimate of the clean LMPSCs is computed by carrying out Bayesian
inference. We employ switching linear dynamical models as an a priori model for
the dynamics of the clean LMPSCs. Further, we derive a stochastic observation
model which relates the clean to the reverberant LMPSCs through a simplified model
of the room impulse response (RIR). This model requires only two parameters, namely
RIR energy and reverberation time, which can be estimated from the captured microphone
signal. The performance of the proposed enhancement technique is studied on the
AURORA5 database and compared to that of constrained maximum-likelihood linear
regression (CMLLR). It is shown by experimental results that our approach significantly
outperforms CMLLR and that up to 80\% of the errors caused by the reverberation
are recovered. In addition to the fact that the approach is compatible with the
standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of
moderate computational complexity and suitable for real time applications.
author:
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Krueger A, Haeb-Umbach R. Model-Based Feature Enhancement for Reverberant Speech
Recognition. IEEE Transactions on Audio, Speech, and Language Processing.
2010;18(7):1692-1707. doi:10.1109/TASL.2010.2049684
apa: Krueger, A., & Haeb-Umbach, R. (2010). Model-Based Feature Enhancement
for Reverberant Speech Recognition. IEEE Transactions on Audio, Speech, and
Language Processing, 18(7), 1692–1707. https://doi.org/10.1109/TASL.2010.2049684
bibtex: '@article{Krueger_Haeb-Umbach_2010, title={Model-Based Feature Enhancement
for Reverberant Speech Recognition}, volume={18}, DOI={10.1109/TASL.2010.2049684},
number={7}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}, pages={1692–1707}
}'
chicago: 'Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
for Reverberant Speech Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing 18, no. 7 (2010): 1692–1707. https://doi.org/10.1109/TASL.2010.2049684.'
ieee: A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant
Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing,
vol. 18, no. 7, pp. 1692–1707, 2010.
mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
for Reverberant Speech Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing, vol. 18, no. 7, 2010, pp. 1692–707, doi:10.1109/TASL.2010.2049684.
short: A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 18 (2010) 1692–1707.
date_created: 2019-07-12T05:29:23Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2049684
intvolume: ' 18'
issue: '7'
keyword:
- ASR
- AURORA5 database
- automatic speech recognition
- Bayesian inference
- belief networks
- CMLLR
- computational complexity
- constrained maximum likelihood linear regression
- least mean squares methods
- LMPSC computation
- logarithmic Mel power spectrum
- maximum likelihood estimation
- Mel frequency cepstral coefficients
- MFCC feature vectors
- microphone signal
- minimum mean square error estimation
- model-based feature enhancement
- regression analysis
- reverberant speech recognition
- reverberation
- RIR energy
- room impulse response
- speech recognition
- stochastic observation model
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf
oa: '1'
page: 1692-1707
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Model-Based Feature Enhancement for Reverberant Speech Recognition
type: journal_article
user_id: '44006'
volume: 18
year: '2010'
...
---
_id: '11937'
abstract:
- lang: eng
text: In automatic speech recognition, hidden Markov models (HMMs) are commonly
used for speech decoding, while switching linear dynamic models (SLDMs) can be
employed for a preceding model-based speech feature enhancement. In this paper,
these model types are combined in order to obtain a novel iterative speech feature
enhancement and recognition architecture. It is shown that speech feature enhancement
with SLDMs can be improved by feeding back information from the HMM to the enhancement
stage. Two different feedback structures are derived. In the first, the posteriors
of the HMM states are used to control the model probabilities of the SLDMs, while
in the second they are employed to directly influence the estimate of the speech
feature distribution. Both approaches lead to improvements in recognition accuracy
both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature
enhancement with SLDMs. It is also shown that a combination with uncertainty decoding
further enhances performance.
author:
- first_name: Stefan
full_name: Windmann, Stefan
last_name: Windmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Windmann S, Haeb-Umbach R. Approaches to Iterative Speech Feature Enhancement
and Recognition. IEEE Transactions on Audio, Speech, and Language Processing.
2009;17(5):974-984. doi:10.1109/TASL.2009.2014894
apa: Windmann, S., & Haeb-Umbach, R. (2009). Approaches to Iterative Speech
Feature Enhancement and Recognition. IEEE Transactions on Audio, Speech, and
Language Processing, 17(5), 974–984. https://doi.org/10.1109/TASL.2009.2014894
bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Approaches to Iterative Speech
Feature Enhancement and Recognition}, volume={17}, DOI={10.1109/TASL.2009.2014894},
number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={974–984}
}'
chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
Feature Enhancement and Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing 17, no. 5 (2009): 974–84. https://doi.org/10.1109/TASL.2009.2014894.'
ieee: S. Windmann and R. Haeb-Umbach, “Approaches to Iterative Speech Feature Enhancement
and Recognition,” IEEE Transactions on Audio, Speech, and Language Processing,
vol. 17, no. 5, pp. 974–984, 2009.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
Feature Enhancement and Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing, vol. 17, no. 5, 2009, pp. 974–84, doi:10.1109/TASL.2009.2014894.
short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 17 (2009) 974–984.
date_created: 2019-07-12T05:31:08Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2014894
intvolume: ' 17'
issue: '5'
keyword:
- AURORA2 databases
- AURORA4 databases
- automatic speech recognition
- feedback structures
- hidden Markov models
- HMM
- iterative methods
- iterative speech feature enhancement
- model probabilities
- speech decoding
- speech enhancement
- speech feature distribution
- speech recognition
- switching linear dynamic models
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-1.pdf
oa: '1'
page: 974-984
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Approaches to Iterative Speech Feature Enhancement and Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...
---
_id: '11938'
abstract:
- lang: eng
text: In this paper, parameter estimation of a state-space model of noise or noisy
speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation
of the state and observation noise covariance from noise-only input data. It is
supposed to be used during the offline training mode of a speech recognizer. Further
a sequential online EM algorithm is developed to adapt the observation noise covariance
on noisy speech cepstra at its input. The estimated parameters are then used in
model-based speech feature enhancement for noise-robust automatic speech recognition.
Experiments on the AURORA4 database lead to improved recognition results with
a linear state model compared to the assumption of stationary noise.
author:
- first_name: Stefan
full_name: Windmann, Stefan
last_name: Windmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Windmann S, Haeb-Umbach R. Parameter Estimation of a State-Space Model of Noise
for Robust Speech Recognition. IEEE Transactions on Audio, Speech, and Language
Processing. 2009;17(8):1577-1590. doi:10.1109/TASL.2009.2023172
apa: Windmann, S., & Haeb-Umbach, R. (2009). Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition. IEEE Transactions on Audio, Speech,
and Language Processing, 17(8), 1577–1590. https://doi.org/10.1109/TASL.2009.2023172
bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition}, volume={17}, DOI={10.1109/TASL.2009.2023172},
number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={1577–1590}
}'
chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a
State-Space Model of Noise for Robust Speech Recognition.” IEEE Transactions
on Audio, Speech, and Language Processing 17, no. 8 (2009): 1577–90. https://doi.org/10.1109/TASL.2009.2023172.'
ieee: S. Windmann and R. Haeb-Umbach, “Parameter Estimation of a State-Space Model
of Noise for Robust Speech Recognition,” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 17, no. 8, pp. 1577–1590, 2009.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition.” IEEE Transactions on Audio,
Speech, and Language Processing, vol. 17, no. 8, 2009, pp. 1577–90, doi:10.1109/TASL.2009.2023172.
short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 17 (2009) 1577–1590.
date_created: 2019-07-12T05:31:09Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2023172
intvolume: ' 17'
issue: '8'
keyword:
- AURORA4 database
- blockwise EM algorithm
- covariance analysis
- linear state model
- noise covariance
- noise-robust automatic speech recognition
- noisy speech cepstra
- offline training mode
- parameter estimation
- speech recognition
- speech recognition equipment
- speech recognizer
- state-space methods
- state-space model
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-2.pdf
oa: '1'
page: 1577-1590
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...
---
_id: '11820'
abstract:
- lang: eng
text: In this paper, we derive an uncertainty decoding rule for automatic speech
recognition (ASR), which accounts for both corrupted observations and inter-frame
correlation. The conditional independence assumption, prevalent in hidden Markov
model-based ASR, is relaxed to obtain a clean speech posterior that is conditioned
on the complete observed feature vector sequence. This is a more informative posterior
than one conditioned only on the current observation. The novel decoding is used
to obtain a transmission-error robust remote ASR system, where the speech capturing
unit is connected to the decoder via an error-prone communication network. We
show how the clean speech posterior can be computed for communication links being
characterized by either bit errors or packet loss. Recognition results are presented
for both distributed and network speech recognition, where in the latter case
common voice-over-IP codecs are employed.
author:
- first_name: Valentin
full_name: Ion, Valentin
last_name: Ion
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Ion V, Haeb-Umbach R. A Novel Uncertainty Decoding Rule With Applications to
Transmission Error Robust Speech Recognition. IEEE Transactions on Audio, Speech,
and Language Processing. 2008;16(5):1047-1060. doi:10.1109/TASL.2008.925879
apa: Ion, V., & Haeb-Umbach, R. (2008). A Novel Uncertainty Decoding Rule With
Applications to Transmission Error Robust Speech Recognition. IEEE Transactions
on Audio, Speech, and Language Processing, 16(5), 1047–1060. https://doi.org/10.1109/TASL.2008.925879
bibtex: '@article{Ion_Haeb-Umbach_2008, title={A Novel Uncertainty Decoding Rule
With Applications to Transmission Error Robust Speech Recognition}, volume={16},
DOI={10.1109/TASL.2008.925879},
number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2008}, pages={1047–1060}
}'
chicago: 'Ion, Valentin, and Reinhold Haeb-Umbach. “A Novel Uncertainty Decoding
Rule With Applications to Transmission Error Robust Speech Recognition.” IEEE
Transactions on Audio, Speech, and Language Processing 16, no. 5 (2008): 1047–60.
https://doi.org/10.1109/TASL.2008.925879.'
ieee: V. Ion and R. Haeb-Umbach, “A Novel Uncertainty Decoding Rule With Applications
to Transmission Error Robust Speech Recognition,” IEEE Transactions on Audio,
Speech, and Language Processing, vol. 16, no. 5, pp. 1047–1060, 2008.
mla: Ion, Valentin, and Reinhold Haeb-Umbach. “A Novel Uncertainty Decoding Rule
With Applications to Transmission Error Robust Speech Recognition.” IEEE Transactions
on Audio, Speech, and Language Processing, vol. 16, no. 5, 2008, pp. 1047–60,
doi:10.1109/TASL.2008.925879.
short: V. Ion, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 16 (2008) 1047–1060.
date_created: 2019-07-12T05:28:53Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/TASL.2008.925879
intvolume: ' 16'
issue: '5'
keyword:
- automatic speech recognition
- bit errors
- codecs
- communication links
- corrupted observations
- decoding
- distributed speech recognition
- error-prone communication network
- feature vector sequence
- hidden Markov model-based ASR
- hidden Markov models
- inter-frame correlation
- Internet telephony
- network speech recognition
- packet loss
- speech posterior
- speech recognition
- transmission error robust speech recognition
- uncertainty decoding
- voice-over-IP codecs
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2008/IoHa08-1.pdf
oa: '1'
page: 1047-1060
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust
Speech Recognition
type: journal_article
user_id: '44006'
volume: 16
year: '2008'
...
---
_id: '11824'
abstract:
- lang: eng
text: Soft-feature based speech recognition, which is an example of uncertainty
decoding, has been proven to be a robust error mitigation method for distributed
speech recognition over wireless channels exhibiting bit errors. In this paper
we extend this concept to packet-oriented transmissions. The a posteriori probability
density function of the lost feature vector, given the closest received neighbours,
is computed. In the experiments, the nearest frame repetition, which is shown
to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts.
Taking the variance into account at the speech recognition stage results in superior
performance compared to classical schemes using point estimates. A computationally
and memory efficient implementation of the proposed packet loss compensation scheme
based on table lookup is presented
author:
- first_name: Valentin
full_name: Ion, Valentin
last_name: Ion
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ion V, Haeb-Umbach R. An Inexpensive Packet Loss Compensation Scheme for Distributed
Speech Recognition Based on Soft-Features. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006). Vol 1. ; 2006:I.
doi:10.1109/ICASSP.2006.1659984'
apa: Ion, V., & Haeb-Umbach, R. (2006). An Inexpensive Packet Loss Compensation
Scheme for Distributed Speech Recognition Based on Soft-Features. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) (Vol.
1, p. I). https://doi.org/10.1109/ICASSP.2006.1659984
bibtex: '@inproceedings{Ion_Haeb-Umbach_2006, title={An Inexpensive Packet Loss
Compensation Scheme for Distributed Speech Recognition Based on Soft-Features},
volume={1}, DOI={10.1109/ICASSP.2006.1659984},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)}, author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2006},
pages={I} }'
chicago: Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation
Scheme for Distributed Speech Recognition Based on Soft-Features.” In IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006),
1:I, 2006. https://doi.org/10.1109/ICASSP.2006.1659984.
ieee: V. Ion and R. Haeb-Umbach, “An Inexpensive Packet Loss Compensation Scheme
for Distributed Speech Recognition Based on Soft-Features,” in IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006,
vol. 1, p. I.
mla: Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation
Scheme for Distributed Speech Recognition Based on Soft-Features.” IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), vol.
1, 2006, p. I, doi:10.1109/ICASSP.2006.1659984.
short: 'V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2006), 2006, p. I.'
date_created: 2019-07-12T05:28:58Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2006.1659984
intvolume: ' 1'
keyword:
- distributed speech recognition
- least mean squares methods
- MAP estimate
- maximum likelihood estimation
- MMSE estimate
- packet loss compensation scheme
- packet switched communication
- posteriori probability density function
- robust error mitigation method
- soft-features
- speech recognition
- table lookup
- voice communication
- wireless channels
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2006/IoHa06-2.pdf
oa: '1'
page: I
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)
status: public
title: An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition
Based on Soft-Features
type: conference
user_id: '44006'
volume: 1
year: '2006'
...
---
_id: '11825'
abstract:
- lang: eng
text: In this paper, we propose an enhanced error concealment strategy at the server
side of a distributed speech recognition (DSR) system, which is fully compatible
with the existing DSR standard. It is based on a Bayesian approach, where the
a posteriori probability density of the error-free feature vector is computed,
given all received feature vectors which are possibly corrupted by transmission
errors. Rather than computing a point estimate, such as the MMSE estimate, and
plugging it into the Bayesian decision rule, we employ uncertainty decoding, which
results in an integration over the uncertainty in the feature domain. In a typical
scenario the communication between the thin client, often a mobile device, and
the recognition server spreads across heterogeneous networks. Both bit errors
on circuit-switched links and lost data packets on IP connections are mitigated
by our approach in a unified manner. The experiments reveal improved robustness
both for small- and large-vocabulary recognition tasks.
author:
- first_name: Valentin
full_name: Ion, Valentin
last_name: Ion
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Ion V, Haeb-Umbach R. Uncertainty decoding for distributed speech recognition
over error-prone networks. Speech Communication. 2006;48(11):1435-1446.
doi:10.1016/j.specom.2006.03.007
apa: Ion, V., & Haeb-Umbach, R. (2006). Uncertainty decoding for distributed
speech recognition over error-prone networks. Speech Communication, 48(11),
1435–1446. https://doi.org/10.1016/j.specom.2006.03.007
bibtex: '@article{Ion_Haeb-Umbach_2006, title={Uncertainty decoding for distributed
speech recognition over error-prone networks}, volume={48}, DOI={10.1016/j.specom.2006.03.007},
number={11}, journal={Speech Communication}, author={Ion, Valentin and Haeb-Umbach,
Reinhold}, year={2006}, pages={1435–1446} }'
chicago: 'Ion, Valentin, and Reinhold Haeb-Umbach. “Uncertainty Decoding for Distributed
Speech Recognition over Error-Prone Networks.” Speech Communication 48,
no. 11 (2006): 1435–46. https://doi.org/10.1016/j.specom.2006.03.007.'
ieee: V. Ion and R. Haeb-Umbach, “Uncertainty decoding for distributed speech recognition
over error-prone networks,” Speech Communication, vol. 48, no. 11, pp.
1435–1446, 2006.
mla: Ion, Valentin, and Reinhold Haeb-Umbach. “Uncertainty Decoding for Distributed
Speech Recognition over Error-Prone Networks.” Speech Communication, vol.
48, no. 11, 2006, pp. 1435–46, doi:10.1016/j.specom.2006.03.007.
short: V. Ion, R. Haeb-Umbach, Speech Communication 48 (2006) 1435–1446.
date_created: 2019-07-12T05:28:59Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1016/j.specom.2006.03.007
intvolume: ' 48'
issue: '11'
keyword:
- Channel error robustness
- Distributed speech recognition
- Soft features
- Uncertainty decoding
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2006/IoHa06-3.pdf
oa: '1'
page: 1435-1446
publication: Speech Communication
status: public
title: Uncertainty decoding for distributed speech recognition over error-prone networks
type: journal_article
user_id: '44006'
volume: 48
year: '2006'
...
---
_id: '11828'
abstract:
- lang: eng
text: 'In this paper we present a comparison of the recently proposed Soft-Feature
Distributed Speech Recognition (SFDSR) with the two evaluated candidate codecs
for Speech Enabled Services over wireless networks: Adaptive Multirate Codec (AMR)
and the ETSI Extended Advanced Front-End for Distributed Speech Recognition (XAFE).
It is shown that SFDSR achieves the best recognition performance on a simulated
GSM transmission, followed by XAFE and AMR.We also present some new results concerning
SFDSR which demonstrate the versatility of the approach. Further, a simple method
is introduced which considerably reduces the computational effort.'
author:
- first_name: Valentin
full_name: Ion, Valentin
last_name: Ion
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ion V, Haeb-Umbach R. A Comparison of Soft-Feature Distributed Speech Recognition
with Candidate Codecs for Speech Enabled Mobile Services. In: IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2005). Vol 1.
; 2005:333-336. doi:10.1109/ICASSP.2005.1415118'
apa: Ion, V., & Haeb-Umbach, R. (2005). A Comparison of Soft-Feature Distributed
Speech Recognition with Candidate Codecs for Speech Enabled Mobile Services. In
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP
2005) (Vol. 1, pp. 333–336). https://doi.org/10.1109/ICASSP.2005.1415118
bibtex: '@inproceedings{Ion_Haeb-Umbach_2005, title={A Comparison of Soft-Feature
Distributed Speech Recognition with Candidate Codecs for Speech Enabled Mobile
Services}, volume={1}, DOI={10.1109/ICASSP.2005.1415118},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2005)}, author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2005},
pages={333–336} }'
chicago: Ion, Valentin, and Reinhold Haeb-Umbach. “A Comparison of Soft-Feature
Distributed Speech Recognition with Candidate Codecs for Speech Enabled Mobile
Services.” In IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2005), 1:333–36, 2005. https://doi.org/10.1109/ICASSP.2005.1415118.
ieee: V. Ion and R. Haeb-Umbach, “A Comparison of Soft-Feature Distributed Speech
Recognition with Candidate Codecs for Speech Enabled Mobile Services,” in IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2005),
2005, vol. 1, pp. 333–336.
mla: Ion, Valentin, and Reinhold Haeb-Umbach. “A Comparison of Soft-Feature Distributed
Speech Recognition with Candidate Codecs for Speech Enabled Mobile Services.”
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP
2005), vol. 1, 2005, pp. 333–36, doi:10.1109/ICASSP.2005.1415118.
short: 'V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2005), 2005, pp. 333–336.'
date_created: 2019-07-12T05:29:02Z
date_updated: 2022-01-06T06:51:10Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2005.1415118
intvolume: ' 1'
keyword:
- adaptive codes
- adaptive multirate codec
- AMR
- distributed speech recognition
- ETSI
- extended advanced front-end
- recognition performance
- SFDSR
- simulated GSM transmission
- soft-feature distributed speech recognition
- speech codecs
- speech coding
- speech recognition
- variable rate codes
- XAFE
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2005/IoHa05-2.pdf
oa: '1'
page: 333-336
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2005)
status: public
title: A Comparison of Soft-Feature Distributed Speech Recognition with Candidate
Codecs for Speech Enabled Mobile Services
type: conference
user_id: '44006'
volume: 1
year: '2005'
...
---
_id: '11931'
abstract:
- lang: eng
text: The paper is concerned with binaural signal processing for a bimodal human-robot
interface with hearing and vision. The two microphone signals are processed to
obtain an enhanced single-channel input signal for the subsequent speech recognizer
and to localize the acoustic source, an important information for establishing
a natural human-robot communication. We utilize a robust adaptive algorithm for
filter-and-sum beamforming (FSB) and extract speaker direction information from
the resulting FIR filter coefficients. Further, particle filtering is applied
which conducts a nonlinear Bayesian tracking of speaker movement. Good location
accuracy can be achieved even in highly reverberant environments. The results
obtained outperform the conventional generalized cross correlation (GCC) method.
author:
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Warsitz E, Haeb-Umbach R. Robust speaker direction estimation with particle
filtering. In: IEEE Workshop on Multimedia Signal Processing (MMSP 2004).
; 2004:367-370. doi:10.1109/MMSP.2004.1436569'
apa: Warsitz, E., & Haeb-Umbach, R. (2004). Robust speaker direction estimation
with particle filtering. In IEEE Workshop on Multimedia Signal Processing (MMSP
2004) (pp. 367–370). https://doi.org/10.1109/MMSP.2004.1436569
bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2004, title={Robust speaker direction
estimation with particle filtering}, DOI={10.1109/MMSP.2004.1436569},
booktitle={IEEE Workshop on Multimedia Signal Processing (MMSP 2004)}, author={Warsitz,
Ernst and Haeb-Umbach, Reinhold}, year={2004}, pages={367–370} }'
chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” In IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 367–70, 2004. https://doi.org/10.1109/MMSP.2004.1436569.
ieee: E. Warsitz and R. Haeb-Umbach, “Robust speaker direction estimation with particle
filtering,” in IEEE Workshop on Multimedia Signal Processing (MMSP 2004),
2004, pp. 367–370.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” IEEE Workshop on Multimedia Signal Processing (MMSP
2004), 2004, pp. 367–70, doi:10.1109/MMSP.2004.1436569.
short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 2004, pp. 367–370.'
date_created: 2019-07-12T05:31:01Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/MMSP.2004.1436569
keyword:
- bimodal human-robot interface
- binaural signal processing
- enhanced single-channel input signal
- filter-and-sum beamforming
- filtering theory
- FIR filter coefficient
- generalized cross correlation method
- microphones
- microphone signal
- nonlinear Bayesian tracking
- particle filtering
- robust adaptive algorithm
- robust speaker direction estimation
- signal processing
- speech enhancement
- speech recognition
- speech recognizer
- user interfaces
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2004/WaHa04.pdf
oa: '1'
page: 367-370
publication: IEEE Workshop on Multimedia Signal Processing (MMSP 2004)
status: public
title: Robust speaker direction estimation with particle filtering
type: conference
user_id: '44006'
year: '2004'
...
---
_id: '39053'
abstract:
- lang: eng
text: Portable devices come with different limitations in user interaction like
limited display size, small keyboard, and different sorts of input and output
capabilities. With the advance of speech recognition and speech synthesis technologies,
their complementary use becomes attractive for mobile devices in order to implement
real multimodal user interaction. However, current systems and formats do not
sufficiently integrate advanced multimodal interactions. We introduce an advanced
generic multimodal interaction and rendering system (MIRS) dedicated for mobile
devices. MIRS incorporates efficient processing of XML specification languages
for limited, mobile devices and comes with the XML-based dialog and interface
specification language (DISL). DISL can be considered as an UIML subset, which
is enhanced by the means of state-oriented dialog specifications. The dialog specification
is based on ODSN (object oriented dialog specification notation), which has been
introduced to define user interface control by means of interaction states with
transition rules.
author:
- first_name: Wolfgang
full_name: Müller, Wolfgang
id: '16243'
last_name: Müller
- first_name: Robbie
full_name: Schäfer, Robbie
last_name: Schäfer
- first_name: Steffen
full_name: Bleul, Steffen
last_name: Bleul
citation:
ama: 'Müller W, Schäfer R, Bleul S. Interactive Multimodal User Interfaces for Mobile
Devices. In: Proceedings of HICCS-37. ; 2004. doi:10.1109/HICSS.2004.1265674'
apa: Müller, W., Schäfer, R., & Bleul, S. (2004). Interactive Multimodal User
Interfaces for Mobile Devices. Proceedings of HICCS-37. 37th Annual Hawaii
International Conference on System Sciences, Waikoloa, HI, USA. https://doi.org/10.1109/HICSS.2004.1265674
bibtex: '@inproceedings{Müller_Schäfer_Bleul_2004, place={Waikoloa, HI, USA}, title={Interactive
Multimodal User Interfaces for Mobile Devices}, DOI={10.1109/HICSS.2004.1265674},
booktitle={Proceedings of HICCS-37}, author={Müller, Wolfgang and Schäfer, Robbie
and Bleul, Steffen}, year={2004} }'
chicago: Müller, Wolfgang, Robbie Schäfer, and Steffen Bleul. “Interactive Multimodal
User Interfaces for Mobile Devices.” In Proceedings of HICCS-37. Waikoloa,
HI, USA, 2004. https://doi.org/10.1109/HICSS.2004.1265674.
ieee: 'W. Müller, R. Schäfer, and S. Bleul, “Interactive Multimodal User Interfaces
for Mobile Devices,” presented at the 37th Annual Hawaii International Conference
on System Sciences, Waikoloa, HI, USA, 2004, doi: 10.1109/HICSS.2004.1265674.'
mla: Müller, Wolfgang, et al. “Interactive Multimodal User Interfaces for Mobile
Devices.” Proceedings of HICCS-37, 2004, doi:10.1109/HICSS.2004.1265674.
short: 'W. Müller, R. Schäfer, S. Bleul, in: Proceedings of HICCS-37, Waikoloa,
HI, USA, 2004.'
conference:
location: Waikoloa, HI, USA
name: 37th Annual Hawaii International Conference on System Sciences
date_created: 2023-01-24T08:46:31Z
date_updated: 2023-01-24T08:46:37Z
department:
- _id: '672'
doi: 10.1109/HICSS.2004.1265674
keyword:
- User interfaces
- Speech recognition
- Streaming media
- Specification languages
- Keyboards
- Speech synthesis
- Rendering (computer graphics)
- Ambient intelligence
- Humans
- Displays
language:
- iso: eng
place: Waikoloa, HI, USA
publication: Proceedings of HICCS-37
publication_identifier:
isbn:
- 0-7695-2056-1
status: public
title: Interactive Multimodal User Interfaces for Mobile Devices
type: conference
user_id: '5786'
year: '2004'
...
---
_id: '11778'
abstract:
- lang: eng
text: In this paper, it is shown that a correlation criterion is the appropriate
criterion for bottom-up clustering to obtain broad phonetic class regression trees
for maximum likelihood linear regression (MLLR)-based speaker adaptation. The
correlation structure among speech units is estimated on the speaker-independent
training data. In adaptation experiments the tree outperformed a regression tree
obtained from clustering according to closeness in acoustic space and achieved
results comparable with those of a manually designed broad phonetic class tree
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Haeb-Umbach R. Automatic generation of phonetic regression class trees for
MLLR adaptation. IEEE Transactions on Speech and Audio Processing. 2001;9(3):299-302.
doi:10.1109/89.906003
apa: Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees
for MLLR adaptation. IEEE Transactions on Speech and Audio Processing,
9(3), 299–302. https://doi.org/10.1109/89.906003
bibtex: '@article{Haeb-Umbach_2001, title={Automatic generation of phonetic regression
class trees for MLLR adaptation}, volume={9}, DOI={10.1109/89.906003},
number={3}, journal={IEEE Transactions on Speech and Audio Processing}, author={Haeb-Umbach,
Reinhold}, year={2001}, pages={299–302} }'
chicago: 'Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class
Trees for MLLR Adaptation.” IEEE Transactions on Speech and Audio Processing
9, no. 3 (2001): 299–302. https://doi.org/10.1109/89.906003.'
ieee: R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for
MLLR adaptation,” IEEE Transactions on Speech and Audio Processing, vol.
9, no. 3, pp. 299–302, 2001.
mla: Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees
for MLLR Adaptation.” IEEE Transactions on Speech and Audio Processing,
vol. 9, no. 3, 2001, pp. 299–302, doi:10.1109/89.906003.
short: R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001)
299–302.
date_created: 2019-07-12T05:28:04Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/89.906003
intvolume: ' 9'
issue: '3'
keyword:
- acoustic space
- adaptation experiments
- automatic generation
- bottom-up clustering
- broad phonetic class regression trees
- correlation criterion
- correlation methods
- maximum likelihood estimation
- maximum likelihood linear regression based speaker adaptation
- MLLR adaptation
- pattern clustering
- phonetic regression class trees
- speaker-independent training data
- speech recognition
- speech units
- statistical analysis
- trees (mathematics)
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2001/Ha01.pdf
oa: '1'
page: 299-302
publication: IEEE Transactions on Speech and Audio Processing
status: public
title: Automatic generation of phonetic regression class trees for MLLR adaptation
type: journal_article
user_id: '44006'
volume: 9
year: '2001'
...
---
_id: '11869'
abstract:
- lang: eng
text: Amongst several data driven approaches for designing filters for the time
sequence of spectral parameters, the linear discriminant analysis (LDA) based
method has been proposed for automatic speech recognition. Here we apply LDA-based
filter design to cepstral features, which better match the inherent assumption
of this method that feature vector components are uncorrelated. Extensive recognition
experiments have been conducted both on the standard TIMIT phone recognition task
and on a proprietary 130-words command word task under various adverse environmental
conditions, including reverberant data with real-life room impulse responses and
data processed by acoustic echo cancellation algorithms. Significant error rate
reductions have been achieved when applying the novel long-range feature filters
compared to standard approaches employing cepstral mean normalization and delta
and delta-delta features, in particular when facing acoustic echo cancellation
scenarios and room reverberation. For example, the phone accuracy on reverberated
TIMIT data could be increased from 50.7\% to 56.0\%
author:
- first_name: M.
full_name: Lieb, M.
last_name: Lieb
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Lieb M, Haeb-Umbach R. LDA derived cepstral trajectory filters in adverse
environmental conditions. In: IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP 2000). Vol 2. ; 2000:II1105-II1108 vol.2. doi:10.1109/ICASSP.2000.859157'
apa: Lieb, M., & Haeb-Umbach, R. (2000). LDA derived cepstral trajectory filters
in adverse environmental conditions. In IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2000) (Vol. 2, pp. II1105-II1108 vol.2).
https://doi.org/10.1109/ICASSP.2000.859157
bibtex: '@inproceedings{Lieb_Haeb-Umbach_2000, title={LDA derived cepstral trajectory
filters in adverse environmental conditions}, volume={2}, DOI={10.1109/ICASSP.2000.859157},
booktitle={IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2000)}, author={Lieb, M. and Haeb-Umbach, Reinhold}, year={2000}, pages={II1105-II1108
vol.2} }'
chicago: Lieb, M., and Reinhold Haeb-Umbach. “LDA Derived Cepstral Trajectory Filters
in Adverse Environmental Conditions.” In IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2000), 2:II1105-II1108 vol.2, 2000.
https://doi.org/10.1109/ICASSP.2000.859157.
ieee: M. Lieb and R. Haeb-Umbach, “LDA derived cepstral trajectory filters in adverse
environmental conditions,” in IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP 2000), 2000, vol. 2, pp. II1105-II1108 vol.2.
mla: Lieb, M., and Reinhold Haeb-Umbach. “LDA Derived Cepstral Trajectory Filters
in Adverse Environmental Conditions.” IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2000), vol. 2, 2000, pp. II1105-II1108
vol.2, doi:10.1109/ICASSP.2000.859157.
short: 'M. Lieb, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2000), 2000, pp. II1105-II1108 vol.2.'
date_created: 2019-07-12T05:29:50Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2000.859157
intvolume: ' 2'
keyword:
- acoustic echo cancellation algorithms
- adverse environmental conditions
- automatic speech recognition
- cepstral analysis
- cepstral features
- cepstral mean normalization
- command word task
- delta-delta features
- delta features
- echo suppression
- error rate reductions
- feature vector components
- FIR filters
- LDA derived cepstral trajectory filters
- linear discriminant analysis
- long-range feature filters
- phone accuracy
- real-life room impulse responses
- reverberant data
- spectral parameters
- speech recognition
- standard TIMIT phone recognition task
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2000/LiHa00.pdf
oa: '1'
page: II1105-II1108 vol.2
publication: IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2000)
status: public
title: LDA derived cepstral trajectory filters in adverse environmental conditions
type: conference
user_id: '44006'
volume: 2
year: '2000'
...