[{"citation":{"bibtex":"@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based significance decoding}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">10.1109/ICASSP.2013.6638984</a>}, 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} }","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.","mla":"Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>, 2013, pp. 6827–31, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">10.1109/ICASSP.2013.6638984</a>.","apa":"Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., &#38; Haeb-Umbach, R. (2013). GMM-based significance decoding. In <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on</i> (pp. 6827–6831). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">https://doi.org/10.1109/ICASSP.2013.6638984</a>","ama":"Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance decoding. In: <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>. ; 2013:6827-6831. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">10.1109/ICASSP.2013.6638984</a>","chicago":"Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant, and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>, 6827–31, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6638984\">https://doi.org/10.1109/ICASSP.2013.6638984</a>.","ieee":"A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based significance decoding,” in <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on</i>, 2013, pp. 6827–6831."},"page":"6827-6831","year":"2013","publication_identifier":{"issn":["1520-6149"]},"doi":"10.1109/ICASSP.2013.6638984","title":"GMM-based significance decoding","author":[{"full_name":"Abdelaziz, Ahmed H.","last_name":"Abdelaziz","first_name":"Ahmed H."},{"first_name":"Steffen","full_name":"Zeiler, Steffen","last_name":"Zeiler"},{"full_name":"Kolossa, Dorothea","last_name":"Kolossa","first_name":"Dorothea"},{"first_name":"Volker","last_name":"Leutnant","full_name":"Leutnant, Volker"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"date_created":"2019-07-12T05:26:53Z","date_updated":"2022-01-06T06:51:07Z","status":"public","abstract":[{"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.","lang":"eng"}],"type":"conference","publication":"Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on","language":[{"iso":"eng"}],"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"],"user_id":"44006","department":[{"_id":"54"}],"_id":"11716"},{"related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf","description":"Poster","relation":"supplementary_material"}]},"publication_identifier":{"issn":["1520-6149"]},"citation":{"chicago":"Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.” In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 3352–56, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">https://doi.org/10.1109/ICASSP.2013.6638279</a>.","ieee":"A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations,” in <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3352–3356.","ama":"Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In: <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:3352-3356. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">10.1109/ICASSP.2013.6638279</a>","mla":"Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.” <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3352–56, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">10.1109/ICASSP.2013.6638279</a>.","bibtex":"@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">10.1109/ICASSP.2013.6638279</a>}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013}, pages={3352–3356} }","short":"A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.","apa":"Chinaev, A., &#38; Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations. In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i> (pp. 3352–3356). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638279\">https://doi.org/10.1109/ICASSP.2013.6638279</a>"},"page":"3352-3356","year":"2013","date_created":"2019-07-12T05:27:20Z","author":[{"last_name":"Chinaev","full_name":"Chinaev, Aleksej","first_name":"Aleksej"},{"last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242","first_name":"Reinhold"}],"date_updated":"2022-01-06T06:51:08Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf"}],"doi":"10.1109/ICASSP.2013.6638279","title":"MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations","type":"conference","publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","status":"public","abstract":[{"text":"In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner.","lang":"eng"}],"user_id":"44006","department":[{"_id":"54"}],"_id":"11740","language":[{"iso":"eng"}],"keyword":["Gaussian noise","maximum likelihood estimation","parameter estimation","GMM parameter","Gaussian mixture model","MAP estimation","Map-based estimation","maximum a-posteriori estimation","maximum likelihood technique","noisy observation","sequential estimation framework","white Gaussian noise","Additive noise","Gaussian mixture model","Maximum likelihood estimation","Noise measurement","Gaussian mixture model","Maximum a posteriori estimation","Maximum likelihood estimation"]}]