Abstract
Nowadays, Tibetan speech synthesis based on neural network has become the mainstream synthesis method. Among them, the griffin-lim vocoder is widely used in Tibetan speech synthesis because of its relatively simple synthesis.Aiming at the problem of low fidelity of griffin-lim vocoder, this paper uses WaveNet vocoder instead of griffin-lim for Tibetan speech synthesis.This paper first uses convolution operation and attention mechanism to extract sequence features.And then uses linear projection and feature amplification module to predict mel spectrogram.Finally, use WaveNet vocoder to synthesize speech waveform. Experimental data shows that our model has a better performance in Tibetan speech synthesis.
Highlights
The speech synthesis method based on neural network greatly reduces the error rate of speech synthesis because the neural network unit has independent learning and back propagation capabilities, and the synthesized speech is closer to the human voice
Based on the literature [7], this paper proposes a Tibetan speech synthesis method based on improved neural network.By constructing an improved neural network,using WaveNet
Due to Ando Tibetan has no tonal characteristics [10], and there are similar pronunciations in the 30 consonants, such as ཅ and ཇ, ཨ and འ, etc.In order to better distinguish similar pronunciations and make the synthesized Tibetan language more natural, this paper proposes an improved neural networks for Tibetan speech synthesis
Summary
The speech synthesis method based on neural network greatly reduces the error rate of speech synthesis because the neural network unit has independent learning and back propagation capabilities, and the synthesized speech is closer to the human voice. As an important part of Chinese information processing, Tibetan speech synthesis is the key and difficulty of Tibetan intelligent human-computer interaction. It started late, it has gradually from the wave-splicing-based Tibetan speech synthesis [5] and the statistical parameter-based Tibetan speech synthesis [6] into Tibetan speech synthesis based on neural network[7,8].In 2019, the literature[7] first proposed speech synthesis based on neural networks, which brought Tibetan speech synthesis into a new era.
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