HMM-based persian speech synthesis using limited adaptation data

Speech synthesis systems provided for the Persian language so far need various large-scale speech corpora to synthesize several target speakers´ voice. Accordingly, synthesizing speech with a small amount of data seems to be essential in Persian. Taking advantage of a speaker adaptation in the speech synthesis systems makes it possible to generate speech with remarkable quality when the data of the speaker are limited. Here we conducted this method for the first time in Persian. This paper describes speaker adaptation based on Hidden Markov Models (HMMs) in Persian speech synthesis system for FARsi Speech DATabase (FARSDAT). In this regard, we prepared the whole FARSDAT, then for synthesizing speech with arbitrary speaker characteristics, we trained the average voice units; afterward, the adapted model was obtained by transforming the average voice model. We demonstrate that a few speech data of a target speaker are sufficient to obtain high quality synthetic speech, and we set out synthetic speech which has been generated from adapted models by using only 88 utterances is very close to that from speaker dependent models trained using 355 utterances.