Automatic phonetic segmentation by score predictive model for the corpora of mandarin singing voices
Journal
IEEE Transactions on Audio, Speech and Language Processing
Journal Volume
15
Journal Issue
7
Pages
2151 - 2159
Date Issued
2007
Author(s)
Lin, C.-Y.
Abstract
This paper proposes the concept of a score predictive model (SPM) that can refine the phoneme boundaries obtained by a hidden Markov model (HMM) and dynamic time warping (DTW) for a Mandarin singing voice corpus. An SPM is constructed by using support vector regression. It predicts the score of a phoneme boundary according to the boundary's 58-dimensional feature vector. The correctly identified boundaries of a singing corpus can then be used for corpus-based singing voice synthesis. Several experiments with different settings, including the use of different initial estimates, different acoustic features, and various regression approaches, were designed to verify the feasibility of the proposed approach. Experimental results demonstrate that the proposed SPM is able to effectively refine the results of the HMM and DTW. © 2006 IEEE.
Subjects
Automatic phonetic segmentation; Boundary refinement; Score predictive model (SPM); Singing voice synthesis
Other Subjects
Acoustic features; Automatic phonetic segmentation; Boundary refinement; Dynamic Time Warping; Feature vectors; Initial estimates; Score predictive model (SPM); Singing voice synthesis; Support vector regressions; Hidden Markov models; Linguistics; Predictive control systems; Refining; Software agents
Type
journal article