A STATISTICAL-MODEL BASED FUNDAMENTAL-FREQUENCY SYNTHESIZER FOR MANDARINE SPEECH

Abstract

A novel method based on a statistical model for the fundamental-frequency (F0) synthesis in Mandarin text-to-speech is proposed. Specifically, a statistical model is employed to determine the relationship between F0 contour patterns of syllables and linguistic features representing the context. Parameters of the model were empirically estimated from a large training set of sentential utterances. Phonologic rules are then automatically deduced through the training process and implicitly memorized in the model. In the synthesis process, contextual features are extracted from a given input text, and the best estimates of F0 contour patterns of syllable are then found by a Viterbi algorithm using the well-trained model. This method can be regarded as employing a stochastic grammar to reduce the number of candidates of F0 contour pattern at each decision point of synthesis. Although linguistic features on various levels of input text can be incorporated into the model, only some relevant contextual features extracted from neighboring syllables were used in this study. Performance of this method was examined by simulation using a database composed of nine repetitions of 112 declarative sentential utterances of the same text, all spoken by a single speaker. By closely examining the well-trained model, some evidence was found to show that the declination effect as well as several sandhi rules are implicitly contained in the model. Experimental results show that 77.56% of synthesized F0 contours coincide with the VQ-quantized counterpart of the original natural speech. Naturalness of the synthesized speech was confirmed by an informal listening test.