The Cmu - Mit Reverb Challenge 2014 System : Description and Results

To evaluate state-of-the-art algorithms and draw new insights regarding potential future research directions in distant speech recognition, Kinoshita et al. [1] launched the REverberant Voice Enhancement and Recognition Benchmark Challenge, commonly known as the REVERB Challenge, intended to provide a test bed for researchers to evaluate their methods based on common corpora and evaluation metrics. In this work, we describe our system and present our results on the 2014 REVERB Challenge (RC). Our system is comprised of four primary components: an acoustic speaker tracking system to determine the speaker’s position; this position is used for beamforming to focus on the desired speech while suppressing noise and reverberation; speaker clustering to determine sets of utterances spoken by the same speaker; and a speech recognition engine with speaker adaptation to extract word hypotheses from the enhanced waveforms produced by the beamformer. On the REAL RC evaluation data, our system obtained a word error rate of 39.9% with a single channel of the array, and 16.9% with the best beamformed signal.