FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation

• URL: http://arxiv.org/abs/2505.14351v2
• Date: Sun, 27 Jul 2025 16:13:27 GMT
• Title: FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation
• Authors: Yutong Liu, Ziyue Zhang, Ban Ma-bao, Yuqing Cai, Yongbin Yu, Renzeng Duojie, Xiangxiang Wang, Fan Gao, Cheng Huang, Nyima Tashi,
• Abstract summary: FMSD-TTS is a few-shot, multi-speaker, multi-dialect text-to-speech framework.<n>It synthesizes parallel dialectal speech from limited reference audio and explicit dialect labels.
• Score: 10.73307957038715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tibetan is a low-resource language with minimal parallel speech corpora spanning its three major dialects-\"U-Tsang, Amdo, and Kham-limiting progress in speech modeling. To address this issue, we propose FMSD-TTS, a few-shot, multi-speaker, multi-dialect text-to-speech framework that synthesizes parallel dialectal speech from limited reference audio and explicit dialect labels. Our method features a novel speaker-dialect fusion module and a Dialect-Specialized Dynamic Routing Network (DSDR-Net) to capture fine-grained acoustic and linguistic variations across dialects while preserving speaker identity. Extensive objective and subjective evaluations demonstrate that FMSD-TTS significantly outperforms baselines in both dialectal expressiveness and speaker similarity. We further validate the quality and utility of the synthesized speech through a challenging speech-to-speech dialect conversion task. Our contributions include: (1) a novel few-shot TTS system tailored for Tibetan multi-dialect speech synthesis, (2) the public release of a large-scale synthetic Tibetan speech corpus generated by FMSD-TTS, and (3) an open-source evaluation toolkit for standardized assessment of speaker similarity, dialect consistency, and audio quality.

Related papers

• Cross-Dialect Text-To-Speech in Pitch-Accent Language Incorporating Multi-Dialect Phoneme-Level BERT [29.167336994990542]
  Cross-dialect text-to-speech (CD-TTS) is a task to synthesize learned speakers' voices in non-native dialects. We present a novel TTS model comprising three sub-modules to perform competitively at this task.
  arXiv  Detail & Related papers  (2024-09-11T13:40:27Z)
• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translation [65.13824257448564]
  This paper proposes a textless training method for many-to-many multilingual speech-to-speech translation. By treating the speech units as pseudo-text, we can focus on the linguistic content of the speech. We demonstrate that the proposed UTUT model can be effectively utilized not only for Speech-to-Speech Translation (S2ST) but also for multilingual Text-to-Speech Synthesis (T2S) and Text-to-Speech Translation (T2ST)
  arXiv  Detail & Related papers  (2023-08-03T15:47:04Z)
• DSE-TTS: Dual Speaker Embedding for Cross-Lingual Text-to-Speech [30.110058338155675]
  Cross-lingual text-to-speech (CTTS) is still far from satisfactory as it is difficult to accurately retain the speaker timbres. We propose a novel dual speaker embedding TTS (DSE-TTS) framework for CTTS with authentic speaking style. By combining both embeddings, DSE-TTS significantly outperforms the state-of-the-art SANE-TTS in cross-lingual synthesis.
  arXiv  Detail & Related papers  (2023-06-25T06:46:36Z)
• MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting [16.37243395952266]
  MParrotTTS is a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model. It adapts to a new language with minimal supervised data and generalizes to languages not seen while training the self-supervised backbone. We present extensive results on six languages in terms of speech naturalness and speaker similarity in parallel and cross-lingual synthesis.
  arXiv  Detail & Related papers  (2023-05-19T13:43:36Z)
• ERNIE-SAT: Speech and Text Joint Pretraining for Cross-Lingual Multi-Speaker Text-to-Speech [58.93395189153713]
  We extend the pretraining method for cross-lingual multi-speaker speech synthesis tasks. We propose a speech-text joint pretraining framework, where we randomly mask the spectrogram and the phonemes. Our model shows great improvements over speaker-embedding-based multi-speaker TTS methods.
  arXiv  Detail & Related papers  (2022-11-07T13:35:16Z)
• Cross-lingual Low Resource Speaker Adaptation Using Phonological Features [2.8080708404213373]
  We train a language-agnostic multispeaker model conditioned on a set of phonologically derived features common across different languages. With as few as 32 and 8 utterances of target speaker data, we obtain high speaker similarity scores and naturalness comparable to the corresponding literature.
  arXiv  Detail & Related papers  (2021-11-17T12:33:42Z)
• Bridging the Modality Gap for Speech-to-Text Translation [57.47099674461832]
  End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structure with a single encoder to learn acoustic representation and semantic information simultaneously. We propose a Speech-to-Text Adaptation for Speech Translation model which aims to improve the end-to-end model performance by bridging the modality gap between speech and text.
  arXiv  Detail & Related papers  (2020-10-28T12:33:04Z)
• SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding [61.02342238771685]
  Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions. Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text. We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
  arXiv  Detail & Related papers  (2020-10-05T19:29:49Z)
• Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation [125.59372403631006]
  We propose a semi-supervised learning approach for multi-speaker text-to-speech (TTS) A multi-speaker TTS model can learn from the untranscribed audio via the proposed encoder-decoder framework with discrete speech representation. We found the model can benefit from the proposed semi-supervised learning approach even when part of the unpaired speech data is noisy.
  arXiv  Detail & Related papers  (2020-05-16T15:47:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.