{"id":155575,"date":"2005-03-01T00:00:00","date_gmt":"2005-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/reverberation-reduction-for-improved-speech-recognition\/"},"modified":"2020-06-04T15:38:03","modified_gmt":"2020-06-04T22:38:03","slug":"reverberation-reduction-for-improved-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/reverberation-reduction-for-improved-speech-recognition\/","title":{"rendered":"Reverberation Reduction for Improved Speech Recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper we present a dereverberation algorithm for improving automatic speech recognition (ASR) results with minimal CPU overhead. As the reverberation tail hurts ASR the most, late reverberation is reduced via gain-based spectral subtraction. We use a multi-band decay model with an efficient method to update it in realtime. In reverberant environments the multi-channel version of the proposed algorithm reduces word error rates (WER) up to one half of the way between those of a microphone array only and a close-talk microphone. The four channel implementation requires less than 2% of the CPU power of a modern computer.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present a dereverberation algorithm for improving automatic speech recognition (ASR) results with minimal CPU overhead. As the reverberation tail hurts ASR the most, late reverberation is reduced via gain-based spectral subtraction. We use a multi-band decay model with an efficient method to update it in realtime. In reverberant environments the multi-channel 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