NDLI: Multi-talker speech recognition under ego-motion noise using Missing Feature Theory

Content Provider	IEEE Xplore Digital Library
Author	Ince, G. Nakadai, K. Rodemann, T. Tsujino, H. Imura, J.
Copyright Year	2010
Description	Author affiliation: Honda Research Institute Japan Co., Ltd. 8-1 Honcho, Wakoshi, Saitama 351-0188, Japan (Ince, G.; Nakadai, K.; Tsujino, H.) \|\| Honda Research Institute Europe GmbH, Carl-Legien Strasse 30, 63073 Offenbach, Germany (Rodemann, T.) \|\| Dept. of Mechanical and Environmental Informatics, Tokyo Institute of Technology 2-12-1-W8-1, O-okayama, Meguro-ku, 152-8552, Japan (Imura, J.)
Abstract	This paper presents a system that gives a mobile robot the ability to recognize target speaker's speech, even if the robot performs an action and there are multiple speakers talking in the room. Associated problems to this system are twofold: (1) While the robot is moving, the joints inevitably generate ego-motion noise due to its motors. (2) Recognizing target speech against other interfering speech signals is a difficult task. Since typical solutions to (1) and (2), motor noise suppression and sound source separation, both introduce distortion to the processed signals, the performance of automatic speech recognition (ASR) deteriorates. Instead of removing the ego-motion noise with conventional noise suppression methods, in this work, we investigate methods to eliminate the unreliable parts of the audio features that are contaminated by the ego-motion noise. For this purpose, we model masks that filter unreliable speech features based on the ratio of speech and motor noise energies. We analyze the performance of the proposed technique under various test conditions by comparing it to the performance of existing Missing Feature Theory-based ASR implementations. Finally, we propose an integration framework for two different masks that are designed to eliminate ego noise and to filter the leakage energy of interfering sound sources. We demonstrate that the proposed methods achieve a high ASR accuracy.
Starting Page	982
Ending Page	987
File Size	1588233
Page Count	6
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5650112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Noise Robots Accuracy Speech
Content Type	Text
Resource Type	Article

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