--- _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' ...