Learning Vietnamese-English Code-Switching Speech Synthesis Model Under Limited Code-Switched Data Scenario

Nguyen, Cuong Manh; Phung, Lam Viet; Bui, Cuc Thi; Truong, Trang Van; Nguyen, Huy Tuan

doi:10.1007/978-3-030-89363-7_12

Cited by 2 publications

(1 citation statement)

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“…New prosodic features (e.g., phrase breaks) were investigated, which showed their efficacy in improving the naturalness of Vietnamese hidden Markov models (HMM)based TTS systems [3,4]. The pronunciation of foreign words is also improved [5]. For postfiltering, it was shown that a global variance scaling method might destroy the tonal information; therefore, exemplar-based voice conversion methods were utilized in postfiltering to preserve the tonal information [6].…”

Section: Introductionmentioning

confidence: 99%

TTS - VLSP 2021: Development of Smartcall Vietnamese Text-to-Speech

Bao

Hoai²,

Hoc³

et al. 2022

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Recent advances in deep learning facilitate the development of end-to-end Vietnamese text-to-speech (TTS) systems with high intelligibility and naturalness in the presence of a clean training corpus. Given a rich source of audio recording data on the Internet, TTS has excellent potential for growth if it can take advantage of this data source. However, the quality of these data is often not sufficient for training TTS systems, e.g., noisy audio. In this paper, we propose an approach that preprocesses noisy found data on the Internet and trains a high-quality TTS model on the processed data. The VLSP-provided training data was thoroughly preprocessed using 1) voice activity detection, 2) automatic speech recognition-based prosodic punctuation insertion, and 3) Spleeter, source separation tool, for separating voice from background music. Moreover, we utilize a state-of-the-art TTS system that takes advantage of the Conditional Variational Autoencoder with the Adversarial Learning model. Our experiment showed that the proposed TTS system trained on the preprocessed data achieved a good result on the provided noisy dataset.

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Section: Introductionmentioning

confidence: 99%