Abstract
The research on Tibetan speech synthesis technology has been mainly focusing on single dialect, and thus there is a lack of research on Tibetan multidialect speech synthesis technology. This paper presents an end-to-end Tibetan multidialect speech synthesis model to realize a speech synthesis system which can be used to synthesize different Tibetan dialects. Firstly, Wylie transliteration scheme is used to convert the Tibetan text into the corresponding Latin letters, which effectively reduces the size of training corpus and the workload of front-end text processing. Secondly, a shared feature prediction network with a cyclic sequence-to-sequence structure is built, which maps the Latin transliteration vector of Tibetan character to Mel spectrograms and learns the relevant features of multidialect speech data. Thirdly, two dialect-specific WaveNet vocoders are combined with the feature prediction network, which synthesizes the Mel spectrum of Lhasa-Ü-Tsang and Amdo pastoral dialect into time-domain waveform, respectively. The model avoids using a large number of Tibetan dialect expertise for processing some time-consuming tasks, such as phonetic analysis and phonological annotation. Additionally, it can directly synthesize Lhasa-Ü-Tsang and Amdo pastoral speech on the existing text annotation. The experimental results show that the synthesized speech of Lhasa-Ü-Tsang and Amdo pastoral dialect based on our proposed method has better clarity and naturalness than the Tibetan monolingual model.
Highlights
Speech synthesis, known as text-to-speech (TTS) technology, mainly solves the problem of converting text information into audible sound information
All dialects use Tibetan characters as written text, but there are some differences in the pronunciation of each dialect, so it is difficult for the people who use different dialects to communicate with each other. ere have been some research studies on LhasaU -Tsang dialect speech synthesis technology [4,5,6,7,8,9,10,11,12]. e end-to-end method [12] has more training advantages than the statistical parameter method, and the synthesis effect is better. ere are few existing research studies on the speech synthesis of Amdo dialect, and only the work [13] applied the statistical parameter speech synthesis (SPSS) based on the hidden Markov model (HMM) for Tibetan Amdo dialect
This paper proposes to use an end-to-end method to implement speech synthesis in Lhasa-U -Tsang and Amdo pastoral dialect, using a single sequence-to-sequence architecture with attention mechanism as the shared feature prediction network for Tibetan multi-dialect and introducing two dialect-specific WaveNet networks to realize the generation of time-domain waveforms
Summary
Known as text-to-speech (TTS) technology, mainly solves the problem of converting text information into audible sound information. For the multilingual speech synthesis, the research works mainly use unit-selection concatenative synthesis technique, SPSS based on HMM, and deep learning technology. This paper proposes to use an end-to-end method to implement speech synthesis in Lhasa-U -Tsang and Amdo pastoral dialect, using a single sequence-to-sequence (seq2seq) architecture with attention mechanism as the shared feature prediction network for Tibetan multi-dialect and introducing two dialect-specific WaveNet networks to realize the generation of time-domain waveforms. 2. Model Architecture e end-to-end speech synthesis model is mainly composed of two parts: the first part contains a seq2seq feature prediction network containing attention mechanism and the second part contains two dialect-specific WaveNet vocoders based on Mel spectrogram.
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