MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting

• URL: http://arxiv.org/abs/2305.11926v1
• Date: Fri, 19 May 2023 13:43:36 GMT
• Title: MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting
• Authors: Neil Shah, Vishal Tambrahalli, Saiteja Kosgi, Niranjan Pedanekar, Vineet Gandhi
• Abstract summary: 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.
• Score: 16.37243395952266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model that can produce high-quality speech. Benefiting from a modularized training paradigm exploiting self-supervised speech representations, MParrotTTS adapts to a new language with minimal supervised data and generalizes to languages not seen while training the self-supervised backbone. Moreover, without training on any bilingual or parallel examples, MParrotTTS 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 on six languages in terms of speech naturalness and speaker similarity in parallel and cross-lingual synthesis. The proposed model outperforms the state-of-the-art multilingual TTS models and baselines, using only a small fraction of supervised training data. Speech samples from our model can be found at https://paper2438.github.io/tts/

Related papers

• 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)
• AudioPaLM: A Large Language Model That Can Speak and Listen [79.44757696533709]
  We introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models. It can process and generate text and speech with applications including speech recognition and speech-to-speech translation.
  arXiv  Detail & Related papers  (2023-06-22T14:37:54Z)
• Textless Speech-to-Speech Translation With Limited Parallel Data [51.3588490789084]
  PFB is a framework for training textless S2ST models that require just dozens of hours of parallel speech data. We train and evaluate our models for English-to-German, German-to-English and Marathi-to-English translation on three different domains.
  arXiv  Detail & Related papers  (2023-05-24T17:59:05Z)
• ParrotTTS: Text-to-Speech synthesis by exploiting self-supervised representations [27.157701195636477]
  ParrotTTS is a modularized text-to-speech synthesis model. It can train a multi-speaker variant effectively using transcripts from a single speaker. It adapts to a new language in low resource setup and generalizes to languages not seen while training the self-supervised backbone.
  arXiv  Detail & Related papers  (2023-03-01T17:23:12Z)
• Multilingual Multiaccented Multispeaker TTS with RADTTS [21.234787964238645]
  We present a multilingual, multiaccented, multispeaker speech synthesis model based on RADTTS. We demonstrate an ability to control synthesized accent for any speaker in an open-source dataset comprising of 7 accents.
  arXiv  Detail & Related papers  (2023-01-24T22:39:04Z)
• 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)
• Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech [37.942466944970704]
  This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models. To train a TTS model from various types of speech and text data, different training schemes are designed to handle supervised (TTS and ASR data) and unsupervised (untranscribed speech and unspoken text) datasets. Experimental evaluation shows that multilingual TTS models trained on Virtuoso can achieve significantly better naturalness and intelligibility than baseline ones in seen languages.
  arXiv  Detail & Related papers  (2022-10-27T14:09:48Z)
• Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification [73.5497360800395]
  We develop an end-to-end system that supports multiple languages. We exploit knowledge from a pre-trained multi-lingual natural language processing model.
  arXiv  Detail & Related papers  (2021-09-28T04:43:11Z)
• Cross-lingual Multispeaker Text-to-Speech under Limited-Data Scenario [10.779568857641928]
  This paper presents an extension on Tacotron2 to achieve bilingual multispeaker speech synthesis. We achieve cross-lingual synthesis, including code-switching cases, between English and Mandarin for monolingual speakers.
  arXiv  Detail & Related papers  (2020-05-21T03:03:34Z)
• 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.