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Researchers demonstrate "WaveNet" neural network, claim large improvements in TTS

Accepted submission by f4r mailto:f4r.hanakodlmg@gmail.com at 2016-09-11 13:08:19
Software
Researchers at Google DeepMind have released a paper [google.com] (PDF) and writeup [deepmind.com] on their new "WaveNet" neural network. WaveNet is able to generate speech that arguably sounds far better than current text-to-speech programs, and was also used to synthesize other audio, such as piano music.

We trained WaveNet using some of Google’s TTS datasets so we could evaluate its performance. The following figure shows the quality of WaveNets on a scale from 1 to 5, compared with Google’s current best TTS systems (parametric and concatenative), and with human speech using Mean Opinion Scores (MOS). MOS are a standard measure for subjective sound quality tests, and were obtained in blind tests with human subjects (from over 500 ratings on 100 test sentences). As we can see, WaveNets reduce the gap between the state of the art and human-level performance by over 50% for both US English and Mandarin Chinese.

For both Chinese and English, Google’s current TTS systems are considered among the best worldwide, so improving on both with a single model is a major achievement.

There are multiple audio samples included on the writeup page.


Original Submission