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Acoustic Modeling for Speech Synthesis: from HMM to RNN

IEEE ASRU, Scottsdale, Arizona, U.S.A. (2015)


Statistical parametric speech synthesis (SPSS) combines an acoustic model and a vocoder to render speech given a text. Typically decision tree-clustered context-dependent hidden Markov models (HMMs) are employed as the acoustic model, which represent a relationship between linguistic and acoustic features. There have been attempts to replace the HMMs by alternative acoustic models, which provide trajectory and context modeling. Recently, artificial neural network-based acoustic models, such as deep neural networks, mixture density networks, and recurrent neural networks (RNNs), showed significant improvements over the HMM-based one. This talk reviews the progress of acoustic modeling in SPSS from the HMM to the RNN.

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