mlsanityData to 13 Oct 2026

ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations

arXiv 2303.01261 ↗First posted 1 Mar 2023cs.CL cs.SD eess.AScited by 4

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Abstract (arXiv)

We present ParrotTTS, a modularized text-to-speech synthesis model leveraging disentangled self-supervised speech representations. It can train a multi-speaker variant effectively using transcripts from a single speaker. ParrotTTS adapts to a new language in low resource setup and generalizes to languages not seen while training the self-supervised backbone. Moreover, without training on bilingual or parallel examples, ParrotTTS can transfer voices across languages while preserving the speaker specific characteristics, e.g., synthesizing fluent Hindi speech using a French speaker's voice and accent. We present extensive results in monolingual and multi-lingual scenarios. ParrotTTS outperforms state-of-the-art multi-lingual TTS models using only a fraction of paired data as latter.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 39 of its 60 references that are in the map. First posted on arXiv 1 Mar 2023.

Builds on

39 of 60 references.

References · 39

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27 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesiscited by 41
  2. 2025Voice Cloning: Comprehensive Surveycited by 4
  3. 2024kNN Retrieval for Simple and Effective Zero-Shot Multi-speaker Text-to-Speechcited by 3
  4. 2024Advancing NAM-to-Speech Conversion with Novel Methods and the MultiNAM Datasetcited by 0