Speaker verification based processing for robust ASR in co-channel speech scenarios

Abstract

Co-channel speech, which occurs in monaural audio recordings of two or more overlapping talkers, poses a great challenge for automatic speech applications. Automatic speech recognition (ASR) performance, in particular, has been shown to degrade significantly in the presence of a competing talker. In this paper, assuming a known target talker scenario, we present two different masking strategies based on speaker verification to alleviate the impact of the competing talker (a.k.a. masker) interference on ASR performance. In the first approach, frame-level speaker verification likelihoods are used as reliability measures that control the degree to which each frame contributes to the Viterbi search, while in the second approach time-frequency (T-F) level speaker verification scores form soft masks for speech separation. Effectiveness of the two strategies, both individually and in combination, are evaluated in the context of ASR tasks with speech mixtures at various signal-to-interference ratios (SIR), ranging from 6 dB to -9 dB. Experimental results indicate efficacy of the proposed speaker verification based solutions in mitigating the impact of the competing talker interference on ASR performance. Combination of the two masking techniques yields reductions as large as 43% in word error rate.