SRI Authors: Andreas Kathol
Abstract
Reverberation in speech degrades the performance of speech recognition systems, leading to higher word error rates. Human listeners can often ignore reverberation, indicating that the auditory system somehow compensates for reverberation degradations. In this work, we present robust acoustic features motivated by the knowledge gained from human speech perception and production, and we demonstrate that these features provide reasonable robustness to reverberation effects compared to traditional mel-filterbank-based features. Using a single-feature system trained with the data distributed through the REVERB 2014 challenge on automatic speech recognition, we show a modest 12 pct. and 0.2 pct. relative reduction in word error rate (WER) compared to the mel-scale-feature-based […]
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