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A New Feature for Speech Segments Extraction with Hidden Markov Models

숨은마코프모형을 이용하는 음성구간 추출을 위한 특징벡터

Hong, Jeong-Woo;Oh, Chang-Hyuck
홍정우;오창혁

  • Published : 2008.03.30

Abstract

In this paper we propose a new feature, average power, for speech segments extraction with hidden Markov models, which is based on mel frequencies of speech signals. The average power is compared with the mel frequency cepstral coefficients, MFCC, and the power coefficient. To compare performances of three types of features, speech data are collected for words with explosives which are generally known hard to be detected. Experiments show that the average power is more accurate and efficient than MFCC and the power coefficient for speech segments extraction in environments with various levels of noise.

Keywords

Average power;hidden Markov model;Mel frequency cepstral coefficients;power coefficient

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