Deep Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis

This paper proposes a deep learning-based Mandarin-Tibetan cross-lingual speech synthesis to realize both Mandarin speech synthesis and Tibetan speech synthesis under a unique framework. Because Tibetan training corpus is hard to record, we train the acoustic models with a large scale Mandarin multi-speaker corpus and a small scale Tibetan one-speaker corpus. The acoustic models are trained with deep neural network (DNN), hybrid long short-term memory (LSTM), and hybrid bi-directional long short-term memory (BLSTM). We also further extend our Chinese text analyzer by adding a Tibetan text analyzer for generating context-dependent labels from input Chinese or Tibetan sentences. The Tibetan text analyzer includes a text normalization, a novel Tibetan word segmentation that combines a BLSTM with conditional random field, a prosodic boundary prediction, and a grapheme-to-phoneme conversion. We select the initials and the finals of both Mandarin and Tibetan as the speech synthesis units to train a speaker-independent mixed language average voice model (AVM) with DNN, hybrid LSTM, and hybrid BLSTM from Mandarin and Tibetan mixed corpus. Then the speaker adaptation is applied to train speaker-dependent DNN, hybrid LSTM, or hybrid BLSTM models of Mandarin or Tibetan with a small target speaker corpus from an AVM. Finally, we synthesize the Mandarin speech, or Tibetan speech though the speaker-dependent Mandarin or Tibetan models. The experiments show that the hybrid BLSTM-based cross-lingual speech synthesis framework is better than the other two cross-lingual frameworks and the Tibetan monolingual framework. The mixed Tibetan training corpus does not influence the voice quality of synthesized Mandarin speech. Furthermore, the hybrid BLSTM-based cross-lingual speech synthesis framework only needs 60% of the training corpus to synthesize a similar voice as the Tibetan monolingual framework. Therefore, the proposed method can be used for speech synthesis of low resource languages by borrowing the same tremendous resource language's corpus.