Two-Feature Voiced/Unvoiced Classifier Using Wavelet Transform

Abstract

This paper proposes a new wavelet-based algorithm for voice/unvoiced classification of speech segments. The classification process is based on: 1) statistical analysis of the energy-frequency distribution of the speech signal using wavelet transform, and 2) estimation of the short-time zero-crossing rate of the signal. First, the ratio of the average energy in the low-frequency wavelet subbands to that of highest-frequency wavelet subband is computed for each time segment of the pre-emphasised speech using a 4-level dyadic wavelet transform, and compared to a pre-determined threshold. This is followed by measuring the zero- crossing rate of the segment and comparing it to a threshold determined by a continually up-dated value of the median of the zero-crossing rates of the speech signal. An experimentally verified criterion based on the re- sults of the above two comparison processes is then applied to obtain the classification decision. The perform- ance of the algorithm has been evaluated on speech data taken from the TIMIT database, and is shown to yield high classification accuracy and robustness to additive noise. technique for V/UV speech classification using two features of the speech signal: (a) the frequency distribution of the average energy and (b) zero-crossing rate, for each speech segment. The performance of the proposed technique has been evaluated using a large database of both clean speech and speech degraded with additive noise. Following this introduction, the second Section gives an overview of the discrete wavelet transform (DWT). The de- tailed description of the proposed V/UV classification algo- rithm is given next. In the forth Section, the various experi- mental results of the performance evaluation of the algorithm are illustrated and discussed. The article concludes by giving a summary of the proposed algorithm and findings of the evaluation process.