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


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.


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


  1. S. -Y. Yoon, L. Chen and K. Zechner, "Predicting Word Accuracy for the Automatic Speech Recognition of Non-native Speech," Interspeech-2010, pp. 773-776, 2010.
  2. W. Kim and J. H. L. Hansen, "Phonetic Distance Based Confidence Measure," Signal Processing Letters, IEEE vol. 17, no.2, pp. 121-124, Feb. 2010.
  3. S. Ji and W. Kim, "A Study on Speech Measure Analysis for Speech Recognition Accuracy Estimation in Noisy Environments," A Conference of Acoustical Society of Korea, vol. 34, no. 1, pp. 46, May 2015.
  4. S. Ji, J. Cho and W. Kim, "Development of Database Verification System for Automatic Speech Recognition," KCC2015, vol. 34, pp. 719-720, June 2015.
  5. S. Ji and W. Kim, "A Study on Effective Speech Recognition Performance Measure using MFCC Similarity," KSCSP-2015, vol. 32, no. 1, pp.220-222, Aug. 2015.
  6. S. Ji, M. Song, J. Yoon and W. Kim, "Speech Recognition Performance Prediction employing Speech Quality Measure," A Conference of Acoustical Society of Korea, vol. 34, no. 2, pp. 46, Nov. 2015.
  7. STNR technique provided by National Institute of Standards and Technology(NIST) [Internet]. Available:
  8. Y. Hu and P. C. Loizou, "Evaluation of Objective Measure for Speech Enhancement," Audio, Speech, and Language Processing, IEEE Transactions on, vol. 16, no.1, pp. 229-238, Sep. 2008.
  9. Hidden Markov Model Toolkit (HTK) developed by Cambridge University. HTK software and tutorial download page [Internet]. Available:
  10. TIMIT speech database provided by Linguistic Data Consortium(LDC) of University of Pennsylvania [Internet]. Available:


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