Quality Estimation Methodology of Speech Recognition Features

Authors

  • R. Lileikyte Vilnius University
  • L. Telksnys Vilnius University

DOI:

https://doi.org/10.5755/j01.eee.110.4.302

Abstract

The best feature set selection is the key of successful speech recognition system. Quality measure is needed to characterize the chosen feature set. Variety of feature quality metrics are proposed by other authors. However, no guidance is given to choose the appropriate metric. Also no metrics investigations for speech features were made. In the paper the methodology for quality estimation of speech features is presented. Metrics have to be chosen on the ground of their correlation with classification results. Linear Frequency Cepstrum (LFCC), Mel Frequency Cepstrum (MFCC), Perceptual Linear Prediction (PLP) analyses were selected for experiment. The most proper metric was chosen in combination with Dynamic Time Warping (DTW) classifier. Experimental investigation results are presented. Ill. 5, bibl. 18, tabl. 3 (in English; abstracts in English and Lithuanian).

http://dx.doi.org/10.5755/j01.eee.110.4.302

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Published

2011-06-08

How to Cite

Lileikyte, R., & Telksnys, L. (2011). Quality Estimation Methodology of Speech Recognition Features. Elektronika Ir Elektrotechnika, 110(4), 113-116. https://doi.org/10.5755/j01.eee.110.4.302

Issue

Section

SIGNAL TECHNOLOGY