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A New Speech Quality Measure for Speech Database Verification System
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 Title & Authors
A New Speech Quality Measure for Speech Database Verification System
Ji, Seung-eun; Kim, Wooil;
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This paper presents a speech recognition database verification system using speech measures, and describes a speech measure extraction algorithm which is applied to this system. In our previous study, to produce an effective speech quality measure for the system, we propose a combination of various speech measures which are highly correlated with WER (Word Error Rate). The new combination of various types of speech quality measures in this study is more effective to predict the speech recognition performance compared to each speech measure alone. In this paper, we increase the system independency by employing GMM acoustic score instead of HMM score which is obtained by a secondary speech recognition system. The combination with GMM score shows a slightly lower correlation with WER compared to the combination with HMM score, however it presents a higher relative improvement in correlation with WER, which is calculated compared to the correlation of each speech measure alone.
Word error rate;Correlation coefficient;Performance prediction;Speech recognition;Speech quality measure;
 Cited by
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.

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. crossref(new window)

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.

S. Ji, J. Cho and W. Kim, "Development of Database Verification System for Automatic Speech Recognition," KCC2015, vol. 34, pp. 719-720, June 2015.

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.

Tcl Developer Xchange. Tcl/tk Software and download page [Internet]. Available:

SNACK Sound Toolkit developed by KTH Royal Institute of Technology. Snack software and tutorial download page [Internet]. Available:

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. crossref(new window)

Hidden Markov Model Toolkit (HTK) developed by Cambridge University. HTK software and tutorial download page [Internet]. Available:

SPHINX project by Carnegie Mellon University. SPHINX software and tutorial download page [Internet]. Available:

STNR technique provided by National Institute of Standards and Technology(NIST) [Internet]. Available: