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Speech Recognition Accuracy Prediction Using Speech Quality Measure

음성 특성 지표를 이용한 음성 인식 성능 예측

  • Received : 2016.01.25
  • Accepted : 2016.03.02
  • Published : 2016.03.31

Abstract

This paper presents our study on speech recognition performance prediction. Our initial study shows that a combination of speech quality measures effectively improves correlation with Word Error Rate (WER) compared to each speech measure alone. In this paper we demonstrate a new combination of various types of speech quality measures shows more significantly improves correlation with WER compared to the speech measure combination of our initial study. In our study, SNR, PESQ, acoustic model score, and MFCC distance are used as the speech quality measures. This paper also presents our speech database verification system for speech recognition employing the speech measures. We develop a WER prediction system using Gaussian mixture model and the speech quality measures as a feature vector. The experimental results show the proposed system is highly effective at predicting WER in a low SNR condition of speech babble and car noise environments.

Keywords

Word error rate;Correlation coefficient;Performance prediction;Speech recognition;Speech quality measure

References

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Acknowledgement

Supported by : Ministry of Land, Infrastructure and Transport of Korean government