A DNN-based Mandarin-Tibetan cross-lingual speech synthesis

By Weitong Guo, Hongwu Yang, Zhenye Gan. 2018

5 citations recorded by the source corpus.

The paper proposed a deep neural network (DNN)-based Mandarin-Tibetan cross-lingual speech synthesis by adopting speaker adaptation training. The initial and the final are used as the speech synthesis units for both Mandarin and Tibetan to train a set of average voice model(AVM) based on DNN from a large Mandarin multi-speaker corpus and a small Tibetan one-speaker corpus. The speaker adaption is adopted to train a set of speaker-dependent DNN models of Mandarin or Tibetan appended with AVM. The Mandarin speech or Tibetan speech is then synthesized by their respective speaker-dependent DNN acoustic models. Both subjective evaluations and objective tests show that synthesized Tibetan speech by the proposed method are not only better than the traditional Hidden Markov Model(HMM)-based method, but also better than the DNN-based Tibetan speech synthesis with only Tibetan training corpus. Mixed Tibetan training speech have little effect on the quality of synthesized Mandarin speech. Therefore, the proposed method can be applied to the speech synthesis of minority language with rare speech resources.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Sheng Zhao
6. Hongwu Yang
7. Yi Ren
8. Xiaolong Bu

In Signal Processing

1. Xu Tan
2. Tie-Yan Liu
3. Tao Qin
4. Frank K. Soong
5. Hongwu Yang
6. Sheng Zhao
7. Yi Ren
8. Zhou Zhao

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, background): “Since human languages share similar vocal organs, pronunciations [383] and semantic structures [335], pre-training the TTS models on rich-resource languages can help the mapping between text and speech in low-resource languages [42, 390, 12, 60, 98, 336, 435, 241, 428].”
• Deep Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis (2022, methodology): “Although DNN-based speaker adaptation [22], [23] has been adopted in various neural networks-based TTS applications [20], [21], the adaptation method has not been applied to the MandarinTibetan cross-lingual speech synthesis.”