Causal analysis of Speech Recognition failure in adverse environments

Abstract

A common approach to measuring the impact of noise and the effectiveness of noise mitigation (NM) algorithms for Automatic Speech Recognition (ASR) systems is to compare the word error rates (WERs). However, the WER measure does not give much insight into how an NM algorithm affects phoneme-level acoustic characteristics. Such insight can help in tuning the NM parameters and may also lead to reduced research time because the impact of an NM algorithm on ASR can first be investigated on smaller corpora. In this paper, two measures, phoneme error rate (PER) and phoneme confidence score (PCS), are investigated to assess the impact of NM algorithms on the ASR performance. Experimental results using the TIMIT corpus show that both PER and PCS can help identify where the degradation from noise occurs as well as give a useful indication of how an NM algorithm may impact ASR performance. A diagnostic method based on these two measures is also proposed to assess the NM impact on ASR and help improve the NM algorithm performance.