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Effectiveness of discriminative approaches for speech recognition under noisy environments on the 2nd CHiME Challenge

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  • 騒音環境下音声認識に対する識別的アプローチの有効性 : 第2回CHiMEチャレンジ(雑音対策,認識,理解,対話,一般)

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The 2nd CHiME challenge is a difficult two-microphone speech recognition task with non-stationary interference. We investigate the effectiveness of state-of-the-art ASR techniques such as discriminative training, various feature transformations and deep neural networks for reverberated and noisy speech recognition, combined with a simple noise suppression method relying on prior-based binary masking with estimated angle of arrival. Moreover, we propose an augmented discriminative feature transformation that can introduce arbitrary features to a discriminative feature transform, an efficient combination method of discriminative language modeling and minimum Bayes risk decoding in an ASR post-processing stage. These techniques are effective for middle-vocabulary sub-task (Track 2) of CHiME challenge. Our performance is the best among participants.


  • IEICE technical report. Speech

    IEICE technical report. Speech 113 (161), 13-18, 2013-07-18

    The Institute of Electronics, Information and Communication Engineers


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