DeviceTTS: A Small-Footprint, Fast, Stable Network for On-Device Text-to-Speech

By Zhiying Huang, Hao Li, Ming Lei. 2020

11 citations recorded by the source corpus.

With the number of smart devices increasing, the demand for on-device text-to-speech (TTS) increases rapidly. In recent years, many prominent End-to-End TTS methods have been proposed, and have greatly improved the quality of synthesized speech. However, to ensure the qualified speech, most TTS systems depend on large and complex neural network models, and it's hard to deploy these TTS systems on-device. In this paper, a small-footprint, fast, stable network for on-device TTS is proposed, named as DeviceTTS. DeviceTTS makes use of a duration predictor as a bridge between encoder and decoder so as to avoid the problem of words skipping and repeating in Tacotron. As we all know, model size is a key factor for on-device TTS. For DeviceTTS, Deep Feedforward Sequential Memory Network (DFSMN) is used as the basic component. Moreover, to speed up inference, mix-resolution decoder is proposed for balance the inference speed and speech quality. Experiences are done with WORLD and LPCNet vocoder. Finally, with only 1.4 million model parameters and 0.099 GFLOPS, DeviceTTS achieves comparable performance with Tacotron and FastSpeech. As far as we know, the DeviceTTS can meet the needs of most of the devices in practical application.

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. Xinyu Yang
6. Zhaoxi Mu
7. Yizhuo Dong
8. Sheng Zhao

In Signal Processing

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

Citations in the corpus

• A Survey on Neural Speech Synthesis (2021, methodology): “10 DeviceTTS [123] AM ph AR −→Ceps→wav arXiv20 2020.”
• Review of end-to-end speech synthesis technology based on deep learning (2021, methodology): “Since one-to-many regression problems like TTS can benefit from autoregressive decoding, it is also possible to combine the autoregressive method with duration predictor to further improve the stability of TTS models, such as the alignment methods used in DurIAN, Non-Attentive Tacotron [190], Device”