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One challenging problem of robust automatic speech recognition (ASR) is howto measure the goodness of a speech enhancement algorithm (SEA) withoutcalculating the word error rate (WER) due to the high costs of manualtranscriptions, language modeling and decoding process. Traditional measureslike PESQ and STOI for evaluating the speech quality and intelligibility wereverified to have relatively low correlations with WER. In this study, a novelacoustics-guided evaluation (AGE) measure is proposed for estimatingperformance of SEAs for robust ASR. AGE consists of three consecutive steps,namely the low-level representations via the feature extraction, high-levelrepresentations via the nonlinear mapping with the acoustic model (AM), and thefinal AGE calculation between the representations of clean speech and degradedspeech. Specifically, state posterior probabilities from neural network basedAM are adopted for the high-level representations and the cross-entropycriterion is used to calculate AGE. Experiments demonstrate AGE could yieldconsistently highest correlations with WER and give the most accurateestimation of ASR performance compared with PESQ, STOI, and acoustic confidencemeasure using Entropy. Potentially, AGE could be adopted to guide the parameteroptimization of deep learning based SEAs to further improve the recognitionperformance.

Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR