[{"department":[{"_id":"54"}],"keyword":["correlation methods","estimation theory","hidden Markov models","iterative methods","probability","spectral analysis","speech processing","2D HMM","SPP estimates","iterative algorithm","posterior probability estimation","spectral correlation","speech presence probability estimation","state-of-the-art SPP estimation algorithm","temporal correlation","turbo principle","two-dimensional hidden Markov model","Correlation","Decoding","Estimation","Iterative decoding","Noise","Speech","Vectors"],"type":"conference","date_created":"2019-07-12T05:30:45Z","abstract":[{"text":"In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm.","lang":"eng"}],"citation":{"ieee":"D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation,” in <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–867.","apa":"Vu, D. H. T., &#38; Haeb-Umbach, R. (2013). Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i> (pp. 863–867). <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>","mla":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 863–67, doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>.","bibtex":"@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>}, booktitle={38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013}, pages={863–867} }","chicago":"Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.” In <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 863–67, 2013. <a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">https://doi.org/10.1109/ICASSP.2013.6637771</a>.","ama":"Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation. In: <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:863-867. doi:<a href=\"https://doi.org/10.1109/ICASSP.2013.6637771\">10.1109/ICASSP.2013.6637771</a>","short":"D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867."},"publication":"38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)","user_id":"44006","doi":"10.1109/ICASSP.2013.6637771","_id":"11917","language":[{"iso":"eng"}],"page":"863-867","date_updated":"2022-01-06T06:51:12Z","author":[{"first_name":"Dang Hai Tran","last_name":"Vu","full_name":"Vu, Dang Hai Tran"},{"id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold","last_name":"Haeb-Umbach"}],"publication_identifier":{"issn":["1520-6149"]},"status":"public","year":"2013","title":"Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation"},{"date_created":"2019-07-12T05:28:04Z","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)"],"type":"journal_article","department":[{"_id":"54"}],"issue":"3","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"}],"main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2001/Ha01.pdf"}],"language":[{"iso":"eng"}],"doi":"10.1109/89.906003","year":"2001","title":"Automatic generation of phonetic regression class trees for MLLR adaptation","author":[{"full_name":"Haeb-Umbach, Reinhold","last_name":"Haeb-Umbach","first_name":"Reinhold","id":"242"}],"date_updated":"2022-01-06T06:51:08Z","intvolume":"         9","oa":"1","citation":{"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>.","short":"R. Haeb-Umbach, IEEE Transactions on Speech and Audio Processing 9 (2001) 299–302.","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.","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} }","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>","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>."},"page":"299-302","_id":"11778","user_id":"44006","volume":9,"status":"public"}]