Speech recognition in living rooms: Integrated speech enhancement and recognition system based on spatial, spectral and temporal modeling of sounds

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• 作者：Marc Delcroix ; ^{marc.delcroix@lab.ntt.co.jp} ; Keisuke Kinoshita ; Tomohiro Nakatani ; Shoko Araki ; Atsunori Ogawa ; Takaaki Hori ; Shinji Watanabe ; Masakiyo Fujimoto ; Takuya Yoshioka ; Takanobu Oba ; Yotaro Kubo ; Mehrez Souden ; Seong-Jun Hahm ; Atsushi Nakamura
• 关键词：Robust ASR ; Model-based speech enhancement ; Example-based speech enhancement ; Model adaptation ; Dynamic variance adaptation
• 刊名：Computer Speech & Language
• 出版年：2013
• 出版时间：May, 2013
• 年：2013
• 卷：27
• 期：3
• 页码：851-873
• 全文大小：572 K

文摘

Research on noise robust speech recognition has mainly focused on dealing with relatively stationary noise that may differ from the noise conditions in most living environments. In this paper, we introduce a recognition system that can recognize speech in the presence of multiple rapidly time-varying noise sources as found in a typical family living room. To deal with such severe noise conditions, our recognition system exploits all available information about speech and noise; that is spatial (directional), spectral and temporal information. This is realized with a model-based speech enhancement pre-processor, which consists of two complementary elements, a multi-channel speech-noise separation method that exploits spatial and spectral information, followed by a single channel enhancement algorithm that uses the long-term temporal characteristics of speech obtained from clean speech examples. Moreover, to compensate for any mismatch that may remain between the enhanced speech and the acoustic model, our system employs an adaptation technique that combines conventional maximum likelihood linear regression with the dynamic adaptive compensation of the variance of the Gaussians of the acoustic model. Our proposed system approaches human performance levels by greatly improving the audible quality of speech and substantially improving the keyword recognition accuracy.