Robust Voicing Detection and Estimation for HMM-Based Speech Synthesis

摘要

This paper proposes a robust voicing detection and estimation method for Hidden Markov model (HMM)-based speech synthesis system. Impulse-like excitation present in voiced speech is utilized for extracting the fundamental frequency. Zero-frequency filter (ZFF) is used to derive the locations of impulse excitation. The main contribution of this paper is exploitation of size of window used in ZFF for accurate voicing detection and estimation. By adaptively choosing appropriate window size, the strength of excitation for voiced speech is significantly higher compared with unvoiced speech. With suitable threshold on the strength of excitation, accurate voicing detection is performed. In this method, smooth and accurate contour is extracted by frame-wise zero-frequency filtering of speech with appropriate window size. Performance of the proposed method is compared with other existing voicing detection and estimation methods. The proposed voicing detection and estimation method is implemented in HMM-based speech synthesis system. Both objective and subjective evaluation results show that the proposed method is capable of generating good quality speech compared with HMM-based speech synthesis systems developed using voicing detection and estimation methods based on Robust algorithm for pitch tracking and Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum.