Deep Feed-Forward Sequential Memory Networks for Speech Synthesis

By Mengxiao Bi, Heng Lu, Shiliang Zhang, Ming Lei, Zhijie Yan. 2018

14 citations recorded by the source corpus.

The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among the best parametric Text-to-Speech (TTS) systems in terms of the naturalness of generated speech, especially the naturalness in prosody. However, the model complexity and inference cost of BLSTM prevents its usage in many runtime applications. Meanwhile, Deep Feed-forward Sequential Memory Networks (DFSMN) has shown its consistent out-performance over BLSTM in both word error rate (WER) and the runtime computation cost in speech recognition tasks. Since speech synthesis also requires to model long-term dependencies compared to speech recognition, in this paper, we investigate the Deep-FSMN (DFSMN) in speech synthesis. Both objective and subjective experiments show that, compared with BLSTM TTS method, the DFSMN system can generate synthesized speech with comparable speech quality while drastically reduce model complexity and speech generation time.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Xu Tan
2. Tao Qin
3. Tie-Yan Liu
4. Frank K. Soong
5. Xinyu Yang
6. Zhaoxi Mu
7. Yizhuo Dong
8. Sheng Zhao

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “, Flow, GAN, VAE, Diffusion) Besides the above acoustic models, there are a lot of other acoustic models [361, 22, 123, 183, 54], as shown in Table 2.”
• Review of end-to-end speech synthesis technology based on deep learning (2021, methodology): “[14] proposed that the deep feed-forward sequential memory network (DFSMN) [260] with a structure similar to dilated-CNN [151] can be used to replace RNN in the acoustic model.”