Reverberant speech recognition exploiting clarity index estimation

We present single-channel approaches to robust automatic speech recognition (ASR) in reverberant environments based on non-intrusive estimation of the clarity index (C50). Our best performing method includes the estimated value of C50 in the ASR feature vector and also uses C50 to select the most suitable ASR acoustic model according to the reverberation level. We evaluate our method on the REVERB Challenge database employing two different C50 estimators and show that our method outperforms the best baseline of the challenge achieved without unsupervised acoustic model adaptation, i.e. using multi-condition hidden Markov models (HMMs). Our approach achieves a 22.4% relative word error rate reduction in comparison to the best baseline of the challenge.