Zen, H and Braunschweiler, N and Buchholz, S and Gales, MJF and Knill, K and Krstulović, S and Latorre, J (2012) Statistical parametric speech synthesis based on speaker and language factorization. IEEE Transactions on Audio, Speech and Language Processing, 20. pp. 1713-1724. ISSN 1558-7916Full text not available from this repository.
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. © 2012 IEEE.
|Uncontrolled Keywords:||Hidden Markov models (HMMs) Speaker and language factorization Statistical parametric speech synthesis|
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:21|
|Last Modified:||12 Dec 2014 19:04|