---
_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: '11841'
abstract:
- lang: eng
text: Recently, substantial progress has been made in the field of reverberant speech
signal processing, including both single- and multichannel de-reverberation techniques,
and automatic speech recognition (ASR) techniques robust to reverberation. To
evaluate state-of-the-art algorithms and obtain new insights regarding potential
future research directions, we propose a common evaluation framework including
datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques.
The proposed framework will be used as a common basis for the REVERB (REverberant
Voice Enhancement and Recognition Benchmark) challenge. This paper describes the
rationale behind the challenge, and provides a detailed description of the evaluation
framework and benchmark results.
author:
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Takuya
full_name: Yoshioka, Takuya
last_name: Yoshioka
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Emanuel
full_name: Habets, Emanuel
last_name: Habets
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Armin
full_name: Sehr, Armin
last_name: Sehr
- first_name: Walter
full_name: Kellermann, Walter
last_name: Kellermann
- first_name: Roland
full_name: Maas, Roland
last_name: Maas
- first_name: Sharon
full_name: Gannot, Sharon
last_name: Gannot
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
citation:
ama: 'Kinoshita K, Delcroix M, Yoshioka T, et al. The reverb challenge: a common
evaluation framework for dereverberation and recognition of reverberant speech.
In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
. ; 2013:22-23.'
apa: 'Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Habets, E., Haeb-Umbach,
R., … Raj, B. (2013). The reverb challenge: a common evaluation framework for
dereverberation and recognition of reverberant speech. In IEEE Workshop on
Applications of Signal Processing to Audio and Acoustics (pp. 22–23).'
bibtex: '@inproceedings{Kinoshita_Delcroix_Yoshioka_Nakatani_Habets_Haeb-Umbach_Leutnant_Sehr_Kellermann_Maas_et
al._2013, title={The reverb challenge: a common evaluation framework for dereverberation
and recognition of reverberant speech}, booktitle={ IEEE Workshop on Applications
of Signal Processing to Audio and Acoustics }, author={Kinoshita, Keisuke and
Delcroix, Marc and Yoshioka, Takuya and Nakatani, Tomohiro and Habets, Emanuel
and Haeb-Umbach, Reinhold and Leutnant, Volker and Sehr, Armin and Kellermann,
Walter and Maas, Roland and et al.}, year={2013}, pages={22–23} }'
chicago: 'Kinoshita, Keisuke, Marc Delcroix, Takuya Yoshioka, Tomohiro Nakatani,
Emanuel Habets, Reinhold Haeb-Umbach, Volker Leutnant, et al. “The Reverb Challenge:
A Common Evaluation Framework for Dereverberation and Recognition of Reverberant
Speech.” In IEEE Workshop on Applications of Signal Processing to Audio and
Acoustics , 22–23, 2013.'
ieee: 'K. Kinoshita et al., “The reverb challenge: a common evaluation framework
for dereverberation and recognition of reverberant speech,” in IEEE Workshop
on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.'
mla: 'Kinoshita, Keisuke, et al. “The Reverb Challenge: A Common Evaluation Framework
for Dereverberation and Recognition of Reverberant Speech.” IEEE Workshop
on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.'
short: 'K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, E. Habets, R. Haeb-Umbach,
V. Leutnant, A. Sehr, W. Kellermann, R. Maas, S. Gannot, B. Raj, in: IEEE Workshop
on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.'
date_created: 2019-07-12T05:29:17Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
keyword:
- Reverberant speech
- dereverberation
- ASR
- evaluation
- challenge
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/Reverb2013.pdf
oa: '1'
page: ' 22-23 '
publication: ' IEEE Workshop on Applications of Signal Processing to Audio and Acoustics '
status: public
title: 'The reverb challenge: a common evaluation framework for dereverberation and
recognition of reverberant speech'
type: conference
user_id: '44006'
year: '2013'
...
---
_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: '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'
...