TY - CHAP A1 - Yoshioka, Takuya A1 - Sehr, Armin A1 - Delcroix, Marc A1 - Kinoshita, Keisuke A1 - Maas, Roland A1 - Nakatani, Tomohiro A1 - Kellermann, Walter T1 - Survey on approaches to speech recognition in reverberant environments T2 - Asia-Pacific Signal & Information Processing Association annual summit and conference (APSIPA ASC), 2012 : Hollywood, California, USA, 3 - 6 Dec. 2012 N2 - This paper overviews the state of the art in reverberant speech processing from the speech recognition viewpoint. First, it points out that the key to successful reverberant speech recognition is to account for long-term dependencies between reverberant observations obtained from consecutive time frames. Then, a diversity of approaches that exploit the long-term dependencies in various ways is described, ranging from signal and feature dereverberation to acoustic model compensation tailored to reverberation. A framework for classifying those approaches is presented to highlight similarities and differences between them. KW - Speech recognition KW - Hidden Markov models KW - Reverberation KW - Speech KW - Vectors KW - Speech processing Y1 - 2012 SN - 978-0-6157-0050-2 PB - IEEE ER - TY - CHAP A1 - Sehr, Armin A1 - Yoshioka, Takuya A1 - Delcroix, Marc A1 - Kinoshita, Keisuke A1 - Nakatani, Tomohiro A1 - Maas, Roland A1 - Kellermann, Walter T1 - Conditional emission densities for combining speech enhancement and recognition systems T2 - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 25 - 29 August 2013, Lyon, France N2 - A novel framework based on conditional emission densities for hidden Markov models (HMMs) is proposed in this contribution to integrate speech enhancement systems with automatic speech recognition systems. In the training phase, the observed feature vectors, corrupted by background noise and reverberation, together with estimates for the interference as provided by the speech enhancement system are used for training joint densities of the observations and the interference estimates. In the decoding phase, the joint densities are transformed to conditional densities of the observed features given the interference estimates. Thus, front end processing can be exploited for obtaining interference estimates, and the estimation errors can be modeled very effectively in a data-driven way. Connected digit recognition experiments in a simulated reverberant environment show the potential of the proposed approach: HMMs with the proposed conditional densities outperform various configurations of conventional HMMs in the logarithmic melspectral domain. This is a first step towards using conditional densities for creating synergies between front end and back end. Index Terms: speech enhancement, robust speech recognition, dereverberation, conditional HMM emission densities, frame- by-frame model adaptation. Y1 - 2013 U6 - https://doi.org/10.21437/Interspeech.2013-265 SP - 3502 EP - 3506 PB - ISCA ER -