---
_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: '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: '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'
...