XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech (2305.19709v1)

Abstract: We present XPhoneBERT, the first multilingual model pre-trained to learn phoneme representations for the downstream text-to-speech (TTS) task. Our XPhoneBERT has the same model architecture as BERT-base, trained using the RoBERTa pre-training approach on 330M phoneme-level sentences from nearly 100 languages and locales. Experimental results show that employing XPhoneBERT as an input phoneme encoder significantly boosts the performance of a strong neural TTS model in terms of naturalness and prosody and also helps produce fairly high-quality speech with limited training data. We publicly release our pre-trained XPhoneBERT with the hope that it would facilitate future research and downstream TTS applications for multiple languages. Our XPhoneBERT model is available at https://github.com/VinAIResearch/XPhoneBERT

An Overview of XPhoneBERT: Multilingual Phoneme Representation for Text-to-Speech Systems

The paper "XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech" offers a comprehensive paper on a novel multilingual model aimed at improving text-to-speech (TTS) systems. The authors introduce XPhoneBERT, the first multilingual pre-trained model designed specifically for learning phoneme representations, which is tailored for TTS tasks. Unlike traditional models which often focus on text derived embeddings, XPhoneBERT aligns closely with the language-specific phonetic structures, unlocking potential for significant enhancements in synthesized speech quality across diverse languages.

Key Contributions and Methodology

1. Model Architecture and Pre-training: XPhoneBERT adopts the architecture of BERT-base and applies the RoBERTa pre-training approach. This methodology leverages a corpus consisting of 330 million sentences expressing phoneme-level data sourced from nearly 100 languages and locales. Such a large dataset underscores the model's potential for broad applicability, where the cross-linguistic phonetic nuances are incorporated directly in the training phase.
2. Experimental Validation: The experimental evaluation indicates that integrating XPhoneBERT as a phoneme encoder within the neural TTS model VITS results in marked improvements in speech naturalness and prosody. Quantitative results show that the use of XPhoneBERT increases mean opinion score (MOS) by significant margins in both English and Vietnamese languages across different settings, including scenarios with limited training data.
3. Public Release and Application: The authors provide open access to the XPhoneBERT model, thereby supporting future academic pursuits and industrial applications. The model provides a ready-to-deploy solution for multilingual TTS systems, potentially accelerating innovation in environments with scant speech data.

Numerical Results and Claims

The reported improvements in MOS for English from 4.00 to 4.14 and for Vietnamese from 3.74 to 3.89 when trained on full datasets demonstrate XPhoneBERT's efficacy. Remarkably, the MOS for Vietnamese improved drastically from 1.59 to 3.35 in limited data scenarios, showcasing the model's robustness in handling data scarcity, which is a prevalent challenge in TTS development.

Implications and Future Directions

Practical Implications: The release of a multilingual phoneme model like XPhoneBERT could instigate a pivotal shift in TTS systems. By allowing direct use of enriched phonetic representations, developers can feasibly improve speech quality in applications such as virtual assistants, audiobooks, and language learning tools across a multitude of languages.

Theoretical Implications: By embracing a phoneme-centric approach at the pre-training stage, XPhoneBERT suggests a paradigm shift in representation learning for language technologies. This could prompt further inquiries into optimizing phonetic embeddings for more specialized phonological tasks and potentially inspire analogous innovations in related fields.

Looking Ahead: While the current paper emphasizes the multilingual capacity and phonetic fidelity of XPhoneBERT, future work might focus on expanding the language corpus further and refining the model’s efficiency. Additionally, exploring the fusion of such phoneme-aware models with other layers of linguistic context could unlock even greater synergy in TTS systems.

In conclusion, XPhoneBERT represents a substantial advancement in the domain of multilingual TTS technology, effectively addressing challenges related to phonetic representation and multilingual application. Its framework and findings lay critical groundwork for subsequent research, steering the field towards more nuanced and universally applicable TTS solutions.