This paper describes a speaker adaptation technique for style control based on multiple regression hidden semi-Markov model (MRHSMM), In the MRHSMM-based style control technique, when available training data is very small, the resultant model would produce unnatural sounding speech. To overcome this problem, we propose a model adaptation technique for MRHSMM, which is similar to the MLLR adaptation technique used in speech recognition and speech synthesis. We formulate the model adaptation problem for MRHSMM based on a linear transformation framework and derive re-estimation formulas for transformation matrices in ML sense. We also describe the results of subjective evaluation tests.
['Takashi Nose', 'Yoichi Kato', 'Takao Kobayashi']
A Speaker Adaptation Technique for MRHSMM-Based Style Control of Synthetic Speech