Takayoshi Yoshimura

Speech parameter generation algorithms for HMM-based speech synthesis

This paper derives a speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors. In the algorithm, we assume that the state sequence (state and mixture sequence for the multi-mixture case) or a part of the state sequence is unobservable (i.e., hidden or latent). As a result, the algorithm iterates the forward-backward algorithm and the parameter generation algorithm for the case where the state sequence is given. Experimental results show that by using the algorithm, we can reproduce clear formant structure from multi-mixture HMMs as compared with that produced from single-mixture HMMs.

Automatic generation of multiple pronunciations based on neural networks

We propose a method for automatically generating a pronunciation dictionary based on a pronunciation neural network that can predict plausible pronunciations (alternative pronunciations) from the canonical pronunciation. This method can generate multiple forms of alternative pronunciations using the pronunciation network. For generating a sophisticated alternative pronunciation dictionary, two techniques are described: (1) alternative pronunciations with likelihoods and (2) alternative pronunciations for word boundary phonemes. Experimental results on spontaneous speech show that the automatically-derived pronunciation dictionaries give consistently higher recognition rates than a conventional dictionary.

State Duration Modeling for HMM-Based Speech Synthesis

This paper describes the explicit modeling of a state durations probability density function in HMM-based speech synthesis. We redefine, in a statistically correct manner, the probability of staying in a state for a time interval used to obtain the state duration PDF and demonstrate improvements in the duration of synthesized speech.