Precise Voicing Information Extraction in Speech Signals Using the Analytic Signal

Abstract

This paper proposes a voiced - unvoiced measure based on the Analytic Signal computation. This voiced - unvoiced feature can be useful for many speech processing applications. For instance, considering speech recognition, it could be incorporated into commonly used acoustic feature vectors, such as for example the Mel Frequency Cepstral Coefficients (MFCC) and their first two derivatives, in order to improve the performance of the overall system. The evaluation of the developed measure has been performed on the TIMIT database. TIMIT has been manually segmented into phones. The voicing information can easily be derived from this segmentation. It is shown in this paper that the automatic voiced - unvoiced segmentation obtained using the method described in the next sections and the manual voiced - unvoiced segmentation provided by TIMIT are very similar.