SINGLE AND MULTI-CHANNEL APPROACHES FOR DISTANT SPEECH RECOGNITION UNDER NOISY REVERBERANT CONDITIONS: I2R'S SYSTEM DESCRIPTION FOR THE ASpIRE CHALLENGE

In this paper, we introduce the system developed at the Institute for Infocomm Research ((IR)-R-2) for the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. The main components of the system are a front-end processing system consisting of a distributed beamforming algorithm, that performs adaptive weighting and channel elimination, a speech dereverberation approach using a maximum-kurtosis criteria, and a robust voice activity detection (VAD) module based on using the sub-harmonic ratio (SHR). The acoustic back-end consists of a multi-conditional Deep Neural Network (DNN) model that uses speaker adapted features combined with a decoding strategy that performs semi-supervised DNN model adaptation using weighted labels generated by the first-pass decoding output. On the single-microphone evaluation, our system achieved a word error rate (WER) of 44.8%. With the incorporation of beamforming on the multi-microphone evaluation, our system achieved an improvement in WER of over 6% to give the best evaluation result of 38.5%.