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Product of Experts for Statistical Parametric Speech Synthesis

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4 Author(s)
Zen, H. ; Nagoya Inst. of Technol., Nagoya, Japan ; Gales, M.J.F. ; Nankaku, Y. ; Tokuda, K.

Multiple acoustic models are often combined in statistical parametric speech synthesis. Both linear and non-linear functions of an observation sequence are used as features to be modeled. This paper shows that this combination of multiple acoustic models can be expressed as a product of experts (PoE); the likelihoods from the models are scaled, multiplied together, and then normalized. Normally these models are individually trained and only combined at the synthesis stage. This paper discusses a more consistent PoE framework where the models are jointly trained. A training algorithm for PoEs based on linear feature functions and Gaussian experts is derived by generalizing the training algorithm for trajectory HMMs. However for non-linear feature functions or non-Gaussian experts this is not possible, so a scheme based on contrastive divergence learning is described. Experimental results show that the PoE framework provides both a mathematically elegant way to train multiple acoustic models jointly and significant improvements in the quality of the synthesized speech.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:20 ,  Issue: 3 )

Date of Publication:

March 2012

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