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Statistical Parametric Speech Synthesis Based on Speaker and Language Factorization

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7 Author(s)
Heiga Zen ; He is now with Google, London, he was with Toshiba Research Europe, Cambridge, UK ; Norbert Braunschweiler ; Sabine Buchholz ; Mark J. F. Gales
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An increasingly common scenario in building speech synthesis and recognition systems is training on inhomogeneous data. This paper proposes a new framework for estimating hidden Markov models on data containing both multiple speakers and multiple languages. The proposed framework, speaker and language factorization, attempts to factorize speaker-/language-specific characteristics in the data and then model them using separate transforms. Language-specific factors in the data are represented by transforms based on cluster mean interpolation with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by transforms based on constrained maximum-likelihood linear regression. Experimental results on statistical parametric speech synthesis show that the proposed framework enables data from multiple speakers in different languages to be used to: train a synthesis system; synthesize speech in a language using speaker characteristics estimated in a different language; and adapt to a new language.

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IEEE Transactions on Audio, Speech, and Language Processing  (Volume:20 ,  Issue: 6 )