Fundamental frequency contour modeling using HMM and categorical multiple regression technique.

Abstract

This paper proposes a novel fundamental frequency (F0) contour modeling based on statistics aiming at text-to-speech. In the proposed modeling, the F0 contour of a sentence is constructed by statistical minor phrase models. These models consist of a sophisticated integration of local models of normalized pitch patterns and global models of maxima and dynamic ranges. Hidden Markov Model (HMM) is introduced to determine the normalized pitch patterns (pitch-HMM). To determine the maximum and the dynamic range, categorical multiple regression technique (CMRT) is introduced. HMM is a good statistical model which directly represents the F0 contours by several reliable states. Moreover, it is easy to take relative changes of the F0 (ΔF0) and phonetic environments into account. CMRT is a good statistical modeling technique which is able to deal with syntactic structures and acoustic events in a sentence simultaneously. Evaluation on the pitch-HMMs shows accent type identification rate of 91% and RMS error of 9.2 Hz. Evaluation on the maximum and the dynamic range models gives 0.901 and 0.835 for the multiple correlation coefficients, respectively. Finally, the result of the subjective evaluation indicates that the proposed modeling is superior to the conventional modeling.