13th Annual Conference of the International Speech Communication Association

Portland, OR, USA
September 9-13, 2012

Factored MLLR Adaptation Algorithm for HMM-based Expressive TTS

June Sig Sung, Doo Hwa Hong, Hyun Woo Koo, Nam Soo Kim

School of Electrical Engineering and INMC, Seoul National University, Seoul, Korea

One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In our previous work, we proposed factored MLLR (FMLLR) where an MLLR parameter is defined as a function of a control parameter vector. We presented a method to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. To show the effectiveness, we applied the FMLLR to adapt the spectral envelope feature of the reading-style speech to those of the singing voice. In this paper, we apply the FMLLR to the HMM-based expressive speech synthesis task and compare its performance with conventional approaches. In a series of experimental results, the FMLLR shows better performance than conventional methods.

Index Terms: MLLR, MRHSMM, Factored MLLR, expressive speech synthesis, HMM-based speech synthesis

Full Paper

Bibliographic reference.  Sung, June Sig / Hong, Doo Hwa / Koo, Hyun Woo / Kim, Nam Soo (2012): "Factored MLLR adaptation algorithm for HMM-based expressive TTS", In INTERSPEECH-2012, 975-978.