Estimation of speech recognition performance in noisy and reverberant environments using PESQ score and acoustic parameters

The automatic speech recognition (ASR) performance is degraded in noisy and reverberant environments. Although various techniques against degradation of the ASR performance have been proposed, it is difficult to properly apply them in evaluation environments with unknown noisy and reverberant conditions. It is possible to properly apply these techniques for improving the ASR performance if we can estimate the relationship between the ASR performance and degradation factors including both noise and reverberation. In this study, we here propose new noisy and reverberant criteria which are referred as “Noisy and Reverberant Speech Recognition with the PESQ and the D_n (NRSR-PD_n)”. We first designed the “NRSR-PD_n” using the relationships among the D value, the PESQ score, and the ASR performance. We then estimated the ASR performance with the designed criteria “NRSR-PD_n” in evaluation experiments. Experimental evaluations demonstrated that our proposed criteria make the well suited for robustly estimating the ASR performance in noisy and reverberant environments.