HMM-based Speech Synthesis: Fundamentals and Its Recent Advances

Описание: The task of speech synthesis is to convert normal language text into speech. In recent years, hidden Markov model (HMM) has been successfully applied to acoustic modeling for speech synthesis, and HMM-based parametric speech synthesis has become a mainstream speech synthesis method. This method is able to synthesize highly intelligible and smooth speech sounds. Another significant advantage of this model-based parametric approach is that it makes speech synthesis far more flexible compared to the conventional unit selection and waveform concatenation approach. This talk will first introduce the overall HMM synthesis system architecture developed at USTC. Then, some key techniques will be described, including the vocoder, acoustic modeling, parameter generation algorithm, MSD-HMM for F0 modeling, context-dependent model training, etc. Our method will be compared with the unit selection approach and its flexibility in controlling voice characteristics will also be presented. The second part of this talk will describe some recent advances of HMM-based speech synthesis at the USTC speech group. The methods to be described include: 1) articulatory control of HMM-based speech synthesis, which further improves the flexibility of HMM-based speech synthesis by integrating phonetic knowledge, 2) LPS-GV and minimum KLD based parameter generation, which alleviates the over-smoothing of generated spectral features and improves the naturalness of synthetic speech, and 3) hybrid HMM-based/unit-selection approach which achieves excellent performance in the Blizzard Challenge speech synthesis evaluation events of recent years.