Novel wavelet-based pitch estimation and segmentation of non-stationary speech
Dimitrios Charalampidis, Vijay Kura · 2005
This paper introduces a novel method for accurate pitch estimation and speech segmentation, named multi-feature, autocorrelation (ACR) and wavelet technique (MAWT). MAWT uses feature extraction, and ACR applied on linear predictive coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions.