[{"publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","type":"conference","status":"public","abstract":[{"lang":"eng","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."}],"department":[{"_id":"54"}],"user_id":"44006","_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"],"related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf"}]},"publication_identifier":{"issn":["1520-6149"]},"page":"3352-3356","citation":{"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.","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>.","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>","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>","short":"A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.","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} }","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>."},"year":"2013","author":[{"first_name":"Aleksej","full_name":"Chinaev, Aleksej","last_name":"Chinaev"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"date_created":"2019-07-12T05:27:20Z","oa":"1","date_updated":"2022-01-06T06:51:08Z","doi":"10.1109/ICASSP.2013.6638279","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf","open_access":"1"}],"title":"MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations"},{"publication_identifier":{"issn":["1520-6149"]},"related_material":{"link":[{"relation":"supplementary_material","description":"Poster","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf"}]},"year":"2013","citation":{"ama":"Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In: <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>","chicago":"Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 3721–25, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">https://doi.org/10.1109/ICASSP.2013.6638353</a>.","ieee":"M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning,” in <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3721–3725.","bibtex":"@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>}, booktitle={38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013}, pages={3721–3725} }","mla":"Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013, pp. 3721–25, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">10.1109/ICASSP.2013.6638353</a>.","short":"M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.","apa":"Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning. In <i>38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i> (pp. 3721–3725). <a href=\"https://doi.org/10.1109/ICASSP.2013.6638353\">https://doi.org/10.1109/ICASSP.2013.6638353</a>"},"page":"3721-3725","date_updated":"2022-01-06T06:51:09Z","oa":"1","date_created":"2019-07-12T05:28:48Z","author":[{"last_name":"Hoang","full_name":"Hoang, Manh Kha","first_name":"Manh Kha"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"title":"Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf"}],"doi":"10.1109/ICASSP.2013.6638353","type":"conference","publication":"38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)","abstract":[{"text":"In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.","lang":"eng"}],"status":"public","_id":"11816","user_id":"44006","department":[{"_id":"54"}],"keyword":["Gaussian processes","Global Positioning System","convergence","expectation-maximisation algorithm","fingerprint identification","indoor radio","signal classification","wireless LAN","EM algorithm","ML estimation","WiFi indoor positioning","censored Gaussian data classification","clipped data","convergence properties","expectation maximization algorithm","fingerprinting method","maximum likelihood estimation","optimal classification","parameters estimation","portable devices sensitivity","signal strength measurements","wireless LAN positioning systems","Convergence","IEEE 802.11 Standards","Maximum likelihood estimation","Parameter estimation","Position measurement","Training","Indoor positioning","censored data","expectation maximization","signal strength","wireless LAN"],"language":[{"iso":"eng"}]},{"citation":{"ama":"Krueger A, Haeb-Umbach R. MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i>. ; 2011:3596-3599. doi:<a href=\"https://doi.org/10.1109/ICASSP.2011.5946256\">10.1109/ICASSP.2011.5946256</a>","chicago":"Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of Non-Stationary Gaussian Processes from Noisy Observations.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i>, 3596–99, 2011. <a href=\"https://doi.org/10.1109/ICASSP.2011.5946256\">https://doi.org/10.1109/ICASSP.2011.5946256</a>.","ieee":"A. Krueger and R. Haeb-Umbach, “MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i>, 2011, pp. 3596–3599.","apa":"Krueger, A., &#38; Haeb-Umbach, R. (2011). MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i> (pp. 3596–3599). <a href=\"https://doi.org/10.1109/ICASSP.2011.5946256\">https://doi.org/10.1109/ICASSP.2011.5946256</a>","bibtex":"@inproceedings{Krueger_Haeb-Umbach_2011, title={MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2011.5946256\">10.1109/ICASSP.2011.5946256</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2011}, pages={3596–3599} }","short":"A. Krueger, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 2011, pp. 3596–3599.","mla":"Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters of Non-Stationary Gaussian Processes from Noisy Observations.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)</i>, 2011, pp. 3596–99, doi:<a href=\"https://doi.org/10.1109/ICASSP.2011.5946256\">10.1109/ICASSP.2011.5946256</a>."},"page":"3596-3599","year":"2011","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2011/KrHa11.pdf"}],"doi":"10.1109/ICASSP.2011.5946256","title":"MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations","author":[{"first_name":"Alexander","full_name":"Krueger, Alexander","last_name":"Krueger"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","full_name":"Haeb-Umbach, Reinhold","id":"242"}],"date_created":"2019-07-12T05:29:22Z","oa":"1","date_updated":"2022-01-06T06:51:11Z","status":"public","abstract":[{"text":"The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is superposed by additive Gaussian observation errors of known variance. Simulations on artificially generated data demonstrate the superiority of the proposed method. While reducing to the ordinary MAP approach in the absence of observation noise, the improvement becomes the more pronounced the larger the variance of the observation noise. The method is further extended to track the parameters in case of non-stationary Gaussian processes.","lang":"eng"}],"type":"conference","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)","language":[{"iso":"eng"}],"keyword":["Gaussian processes","MAP-based estimation","maximum a posteriori method","maximum likelihood estimation","nonstationary Gaussian processes"],"user_id":"44006","department":[{"_id":"54"}],"_id":"11845"},{"page":"1692-1707","intvolume":"        18","citation":{"short":"A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 18 (2010) 1692–1707.","bibtex":"@article{Krueger_Haeb-Umbach_2010, title={Model-Based Feature Enhancement for Reverberant Speech Recognition}, volume={18}, DOI={<a href=\"https://doi.org/10.1109/TASL.2010.2049684\">10.1109/TASL.2010.2049684</a>}, number={7}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}, pages={1692–1707} }","mla":"Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 18, no. 7, 2010, pp. 1692–707, doi:<a href=\"https://doi.org/10.1109/TASL.2010.2049684\">10.1109/TASL.2010.2049684</a>.","apa":"Krueger, A., &#38; Haeb-Umbach, R. (2010). Model-Based Feature Enhancement for Reverberant Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>18</i>(7), 1692–1707. <a href=\"https://doi.org/10.1109/TASL.2010.2049684\">https://doi.org/10.1109/TASL.2010.2049684</a>","ama":"Krueger A, Haeb-Umbach R. Model-Based Feature Enhancement for Reverberant Speech Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2010;18(7):1692-1707. doi:<a href=\"https://doi.org/10.1109/TASL.2010.2049684\">10.1109/TASL.2010.2049684</a>","chicago":"Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 18, no. 7 (2010): 1692–1707. <a href=\"https://doi.org/10.1109/TASL.2010.2049684\">https://doi.org/10.1109/TASL.2010.2049684</a>.","ieee":"A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 18, no. 7, pp. 1692–1707, 2010."},"volume":18,"author":[{"last_name":"Krueger","full_name":"Krueger, Alexander","first_name":"Alexander"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"oa":"1","date_updated":"2022-01-06T06:51:11Z","doi":"10.1109/TASL.2010.2049684","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf","open_access":"1"}],"type":"journal_article","status":"public","department":[{"_id":"54"}],"user_id":"44006","_id":"11846","issue":"7","year":"2010","date_created":"2019-07-12T05:29:23Z","title":"Model-Based Feature Enhancement for Reverberant Speech Recognition","publication":"IEEE Transactions on Audio, Speech, and Language Processing","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."}],"language":[{"iso":"eng"}],"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"]},{"page":"III-277-III-280","intvolume":"         3","citation":{"ama":"Haeb-Umbach R, Bevermeier M. OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>. Vol 3. ; 2007:III-277-III-280. doi:<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>","ieee":"R. Haeb-Umbach and M. Bevermeier, “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 2007, vol. 3, pp. III-277-III–280.","chicago":"Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, 3:III-277-III–280, 2007. <a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">https://doi.org/10.1109/ICASSP.2007.366526</a>.","bibtex":"@inproceedings{Haeb-Umbach_Bevermeier_2007, title={OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain}, volume={3}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)}, author={Haeb-Umbach, Reinhold and Bevermeier, Maik}, year={2007}, pages={III-277-III–280} }","short":"R. Haeb-Umbach, M. Bevermeier, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), 2007, pp. III-277-III–280.","mla":"Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i>, vol. 3, 2007, pp. III-277-III–280, doi:<a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">10.1109/ICASSP.2007.366526</a>.","apa":"Haeb-Umbach, R., &#38; Bevermeier, M. (2007). OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)</i> (Vol. 3, pp. III-277-III–280). <a href=\"https://doi.org/10.1109/ICASSP.2007.366526\">https://doi.org/10.1109/ICASSP.2007.366526</a>"},"year":"2007","volume":3,"date_created":"2019-07-12T05:28:13Z","author":[{"id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold"},{"full_name":"Bevermeier, Maik","last_name":"Bevermeier","first_name":"Maik"}],"date_updated":"2022-01-06T06:51:08Z","oa":"1","doi":"10.1109/ICASSP.2007.366526","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2007/HaBe07.pdf"}],"title":"OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)","type":"conference","status":"public","abstract":[{"lang":"eng","text":"In this paper we present a novel channel impulse response estimation technique for block-oriented OFDM transmission based on combining estimators: the estimates provided by a Kalman filter operating in the time domain and a Wiener filter in the frequency domain are optimally combined by taking into account their estimated error covariances. The resulting estimator turns out to be identical to the MAP estimator of correlated jointly Gaussian mean vectors. Different variants of the proposed scheme are experimentally investigated in an EEEE 802.11a-like system setup. They compare favourably with known approaches from the literature resulting in reduced mean square estimation error and bit error rate. Further, robustness and complexity issues are discussed"}],"department":[{"_id":"54"}],"user_id":"44006","_id":"11785","language":[{"iso":"eng"}],"keyword":["bit error rate","block-oriented OFDM transmission","channel estimation","channel impulse response estimation","combining estimators","error statistics","frequency domain estimation","Gaussian mean vectors","Gaussian processes","Kalman filter","Kalman filters","MAP estimator","maximum likelihood estimation","OFDM channel estimation","OFDM modulation","time domain estimation","time-frequency analysis","Wiener filter","Wiener filters"]},{"doi":"10.1109/ICASSP.2006.1659984","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2006/IoHa06-2.pdf","open_access":"1"}],"title":"An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features","volume":1,"date_created":"2019-07-12T05:28:58Z","author":[{"first_name":"Valentin","full_name":"Ion, Valentin","last_name":"Ion"},{"first_name":"Reinhold","id":"242","full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach"}],"date_updated":"2022-01-06T06:51:10Z","oa":"1","intvolume":"         1","page":"I","citation":{"apa":"Ion, V., &#38; Haeb-Umbach, R. (2006). An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol. 1, p. I). <a href=\"https://doi.org/10.1109/ICASSP.2006.1659984\">https://doi.org/10.1109/ICASSP.2006.1659984</a>","bibtex":"@inproceedings{Ion_Haeb-Umbach_2006, title={An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features}, volume={1}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2006.1659984\">10.1109/ICASSP.2006.1659984</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}, author={Ion, Valentin and Haeb-Umbach, Reinhold}, year={2006}, pages={I} }","short":"V. Ion, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.","mla":"Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol. 1, 2006, p. I, doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1659984\">10.1109/ICASSP.2006.1659984</a>.","ieee":"V. Ion and R. Haeb-Umbach, “An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006, vol. 1, p. I.","chicago":"Ion, Valentin, and Reinhold Haeb-Umbach. “An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 1:I, 2006. <a href=\"https://doi.org/10.1109/ICASSP.2006.1659984\">https://doi.org/10.1109/ICASSP.2006.1659984</a>.","ama":"Ion V, Haeb-Umbach R. An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I. doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1659984\">10.1109/ICASSP.2006.1659984</a>"},"year":"2006","language":[{"iso":"eng"}],"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"],"department":[{"_id":"54"}],"user_id":"44006","_id":"11824","status":"public","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"}],"publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)","type":"conference"},{"type":"journal_article","publication":"IEEE Transactions on Speech and Audio Processing","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"}],"status":"public","_id":"11778","user_id":"44006","department":[{"_id":"54"}],"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"}],"issue":"3","year":"2001","citation":{"apa":"Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees for MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>, <i>9</i>(3), 299–302. <a href=\"https://doi.org/10.1109/89.906003\">https://doi.org/10.1109/89.906003</a>","bibtex":"@article{Haeb-Umbach_2001, title={Automatic generation of phonetic regression class trees for MLLR adaptation}, volume={9}, DOI={<a href=\"https://doi.org/10.1109/89.906003\">10.1109/89.906003</a>}, number={3}, journal={IEEE Transactions on Speech and Audio Processing}, author={Haeb-Umbach, Reinhold}, year={2001}, pages={299–302} }","short":"R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001) 299–302.","mla":"Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i>, vol. 9, no. 3, 2001, pp. 299–302, doi:<a href=\"https://doi.org/10.1109/89.906003\">10.1109/89.906003</a>.","chicago":"Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees for MLLR Adaptation.” <i>IEEE Transactions on Speech and Audio Processing</i> 9, no. 3 (2001): 299–302. <a href=\"https://doi.org/10.1109/89.906003\">https://doi.org/10.1109/89.906003</a>.","ieee":"R. Haeb-Umbach, “Automatic generation of phonetic regression class trees for MLLR adaptation,” <i>IEEE Transactions on Speech and Audio Processing</i>, vol. 9, no. 3, pp. 299–302, 2001.","ama":"Haeb-Umbach R. Automatic generation of phonetic regression class trees for MLLR adaptation. <i>IEEE Transactions on Speech and Audio Processing</i>. 2001;9(3):299-302. doi:<a href=\"https://doi.org/10.1109/89.906003\">10.1109/89.906003</a>"},"page":"299-302","intvolume":"         9","date_updated":"2022-01-06T06:51:08Z","oa":"1","date_created":"2019-07-12T05:28:04Z","author":[{"first_name":"Reinhold","full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach"}],"volume":9,"title":"Automatic generation of phonetic regression class trees for MLLR adaptation","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2001/Ha01.pdf"}],"doi":"10.1109/89.906003"}]