{"year":"2009","status":"public","intvolume":" 17","author":[{"full_name":"Windmann, Stefan","last_name":"Windmann","first_name":"Stefan"},{"last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold"}],"volume":17,"_id":"11937","title":"Approaches to Iterative Speech Feature Enhancement and Recognition","department":[{"_id":"54"}],"user_id":"44006","citation":{"chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech Feature Enhancement and Recognition.” IEEE Transactions on Audio, Speech, and Language Processing 17, no. 5 (2009): 974–84. https://doi.org/10.1109/TASL.2009.2014894.","short":"S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 974–984.","bibtex":"@article{Windmann_Haeb-Umbach_2009, title={Approaches to Iterative Speech Feature Enhancement and Recognition}, volume={17}, DOI={10.1109/TASL.2009.2014894}, number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={974–984} }","apa":"Windmann, S., & Haeb-Umbach, R. (2009). Approaches to Iterative Speech Feature Enhancement and Recognition. IEEE Transactions on Audio, Speech, and Language Processing, 17(5), 974–984. https://doi.org/10.1109/TASL.2009.2014894","ama":"Windmann S, Haeb-Umbach R. Approaches to Iterative Speech Feature Enhancement and Recognition. IEEE Transactions on Audio, Speech, and Language Processing. 2009;17(5):974-984. doi:10.1109/TASL.2009.2014894","mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech Feature Enhancement and Recognition.” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 5, 2009, pp. 974–84, doi:10.1109/TASL.2009.2014894.","ieee":"S. Windmann and R. Haeb-Umbach, “Approaches to Iterative Speech Feature Enhancement and Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 5, pp. 974–984, 2009."},"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-1.pdf","open_access":"1"}],"language":[{"iso":"eng"}],"type":"journal_article","date_updated":"2022-01-06T06:51:12Z","publication":"IEEE Transactions on Audio, Speech, and Language Processing","abstract":[{"text":"In automatic speech recognition, hidden Markov models (HMMs) are commonly used for speech decoding, while switching linear dynamic models (SLDMs) can be employed for a preceding model-based speech feature enhancement. In this paper, these model types are combined in order to obtain a novel iterative speech feature enhancement and recognition architecture. It is shown that speech feature enhancement with SLDMs can be improved by feeding back information from the HMM to the enhancement stage. Two different feedback structures are derived. In the first, the posteriors of the HMM states are used to control the model probabilities of the SLDMs, while in the second they are employed to directly influence the estimate of the speech feature distribution. Both approaches lead to improvements in recognition accuracy both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature enhancement with SLDMs. It is also shown that a combination with uncertainty decoding further enhances performance.","lang":"eng"}],"oa":"1","keyword":["AURORA2 databases","AURORA4 databases","automatic speech recognition","feedback structures","hidden Markov models","HMM","iterative methods","iterative speech feature enhancement","model probabilities","speech decoding","speech enhancement","speech feature distribution","speech recognition","switching linear dynamic models"],"date_created":"2019-07-12T05:31:08Z","issue":"5","doi":"10.1109/TASL.2009.2014894","page":"974-984"}