Computer Science > Computation and Language
[Submitted on 24 Jun 2024 (v1), last revised 7 Jul 2024 (this version, v3)]
Title:Towards Zero-Shot Text-To-Speech for Arabic Dialects
View PDF HTML (experimental)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{this https URL}\footnote{this https URL}\footnote{this https URL} 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.
Submission history
From: Khai Doan [view email][v1] Mon, 24 Jun 2024 15:58:15 UTC (749 KB)
[v2] Tue, 25 Jun 2024 14:18:21 UTC (749 KB)
[v3] Sun, 7 Jul 2024 15:27:26 UTC (749 KB)
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