Towards Zero-Shot Text-To-Speech for Arabic Dialects

• URL: http://arxiv.org/abs/2406.16751v3
• Date: Sun, 7 Jul 2024 15:27:26 GMT
• Title: Towards Zero-Shot Text-To-Speech for Arabic Dialects
• Authors: Khai Duy Doan, Abdul Waheed, Muhammad Abdul-Mageed,
• Abstract summary: Zero-shot multi-speaker text-to-speech (ZS-TTS) systems have advanced for English, however, it still lags behind due to insufficient resources. We address this gap for Arabic by first adapting an existing dataset to suit the needs of speech synthesis. We employ a set of Arabic dialect identification models to explore the impact of pre-defined dialect labels on improving the ZS-TTS model in a multi-dialect setting.
• Score: 16.10882912169842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Zero-shot multi-speaker text-to-speech (ZS-TTS) systems have advanced for English, however, it still lags behind due to insufficient resources. We address this gap for Arabic, a language of more than 450 million native speakers, by first adapting a sizeable existing dataset to suit the needs of speech synthesis. Additionally, we employ a set of Arabic dialect identification models to explore the impact of pre-defined dialect labels on improving the ZS-TTS model in a multi-dialect setting. Subsequently, we fine-tune the XTTS\footnote{https://docs.coqui.ai/en/latest/models/xtts.html}\footnote{https://medium.com/machine-learns/xtts-v2-new-version-of-the-open-source-text-to-speech-model-af7391 4db81f}\footnote{https://medium.com/@erogol/xtts-v1-techincal-notes-eb83ff05bdc} model, an open-source architecture. We then evaluate our models on a dataset comprising 31 unseen speakers and an in-house dialectal dataset. Our automated and human evaluation results show convincing performance while capable of generating dialectal speech. Our study highlights significant potential for improvements in this emerging area of research in Arabic.

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