The STC text-to-speech system for Blizzard Challenge 2019
By Roman Korostik, Artem Chirkovskiy, Alexey Svischev, I. Kalinovskiy, A. Talanov. 2019
3 citations recorded by the source corpus.
The paper presents text-to-speech system developed at STC for the Blizzard Challenge 2019. This year, the task is to build a TTS system for Mandarin Chinese using found data suitable for expressive TTS. Provided corpus contains 8 hours of speech by a native speaker with text annotations. We describe a neural speech synthesis system for Mandarin Chinese built without any significant prior knowledge about the language. Input text is converted to a sequence of phones using publicly available tools. Then, a sequence of phones is turned into a spectrogram by a Tacotron-based neural network. Finally, the spectrogram is converted into a waveform using a LPCNet-based neural network. Our system is based on learning deep representations and does not explicitly use or predict such features as pitch, duration of every phone, etc. We also discuss our system’s performance in listening tests conducted by organizers of the challenge.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Speech & Audio
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Citations in the corpus
- FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction (2020, methodology): “Instead of adopting cepstrums [4], the LP coefficients were estimated from the mel-spectrograms as in [23].”