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Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends

Zhen-Hua Ling
Shiyin Kang
Mike Schuster
Xiao-Jun Qian
Helen Meng
Li Deng
IEEE Signal Processing Magazine, vol. 32 (2015), pp. 35-52


Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inputs and the acoustic features. Inspired by the intrinsically hierarchical process of human speech production and by the successful application of deep neural networks (DNNs) to automatic speech recognition (ASR), deep learning techniques have also been applied successfully to speech generation, as reported in recent literature.

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