Using Mandarin Training Corpus to Realize a Mandarin-Tibetan Cross-Lingual Emotional Speech Synthesis

Abstract

This paper presents a hidden Markov model (HMM)-based Mandarin-Tibetan cross-lingual emotional speech synthesis by using an emotional Mandarin speech corpus with speaker adaptation. We firstly train a set of average acoustic models by speaker adaptive training with a one-speaker neutral Tibetan corpus and a multi-speaker neutral Mandarin corpus. Then we train a set of speaker dependent acoustic models of target emotion, which are used to synthesize emotional Tibetan or Mandarin speech, by speaker adaptation with the target emotional Mandarin corpus. Subjective evaluations and objective tests show that the method can synthesize both emotional Mandarin speech and emotional Tibetan speech with high naturalness and emotional similarity. Therefore, the method can be adopted to realizing an emotional speech synthesis with exiting emotional training corpus for languages lacking emotional speech resources.