VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation

• URL: http://arxiv.org/abs/2303.07578v1
• Date: Tue, 14 Mar 2023 01:55:41 GMT
• Title: VANI: Very-lightweight Accent-controllable TTS for Native and Non-native speakers with Identity Preservation
• Abstract summary: We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. We utilize the Indic languages dataset, released for LIMMITS 2023 as part of ICASSP Signal Processing Grand Challenge, to synthesize speech in 3 different languages.
• Score: 25.210244564579522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce VANI, a very lightweight multi-lingual accent controllable speech synthesis system. Our model builds upon disentanglement strategies proposed in RADMMM and supports explicit control of accent, language, speaker and fine-grained $F_0$ and energy features for speech synthesis. We utilize the Indic languages dataset, released for LIMMITS 2023 as part of ICASSP Signal Processing Grand Challenge, to synthesize speech in 3 different languages. Our model supports transferring the language of a speaker while retaining their voice and the native accent of the target language. We utilize the large-parameter RADMMM model for Track $1$ and lightweight VANI model for Track $2$ and $3$ of the competition.

Related papers

• TalTech Systems for the Interspeech 2025 ML-SUPERB 2.0 Challenge [4.297070083645049]
  A hybrid language identification system is used, consisting of a pretrained language embedding model and a light-weight speech recognition model with a shared encoder across languages.<n>For speech recognition, three models are used, where only a single model is applied for each language, depending on the training data availability and performance on held-out data.<n>The system obtained the top overall score in the challenge.
  arXiv  Detail & Related papers  (2025-06-02T09:16:09Z)
• CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training [70.31925012315064]
  We present CosyVoice 3, an improved model designed for zero-shot multilingual speech synthesis in the wild.<n>Key features of CosyVoice 3 include a novel speech tokenizer to improve prosody naturalness.<n>Data is expanded from ten thousand hours to one million hours, encompassing 9 languages and 18 Chinese dialects.
  arXiv  Detail & Related papers  (2025-05-23T07:55:21Z)
• Zero-resource Speech Translation and Recognition with LLMs [38.11535502039386]
  We propose to leverage a multilingual Large Language Model (LLM) to perform ST and ASR in languages for which the model has never seen paired audio-text data. We achieve this by using a pre-trained multilingual speech encoder, a multilingual LLM, and a lightweight adaptation module that maps the audio representations to the token embedding space of the LLM.
  arXiv  Detail & Related papers  (2024-12-24T17:37:11Z)
• FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs [63.8261207950923]
  FunAudioLLM is a model family designed to enhance natural voice interactions between humans and large language models (LLMs) At its core are two innovative models: SenseVoice, which handles multilingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, which facilitates natural speech generation with control over multiple languages, timbre, speaking style, and speaker identity. The models related to SenseVoice and CosyVoice have been open-sourced on Modelscope and Huggingface, along with the corresponding training, inference, and fine-tuning codes released on GitHub.
  arXiv  Detail & Related papers  (2024-07-04T16:49:02Z)
• An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios [76.11409260727459]
  This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system. We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance.
  arXiv  Detail & Related papers  (2024-06-13T08:16:52Z)
• 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)
• PolyVoice: Language Models for Speech to Speech Translation [50.31000706309143]
  PolyVoice is a language model-based framework for speech-to-speech translation (S2ST) We use discretized speech units, which are generated in a fully unsupervised way. For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
  arXiv  Detail & Related papers  (2023-06-05T15:53:15Z)
• 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)
• Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling [92.55131711064935]
  We propose a cross-lingual neural language model, VALL-E X, for cross-lingual speech synthesis. VALL-E X inherits strong in-context learning capabilities and can be applied for zero-shot cross-lingual text-to-speech synthesis and zero-shot speech-to-speech translation tasks. It can generate high-quality speech in the target language via just one speech utterance in the source language as a prompt while preserving the unseen speaker's voice, emotion, and acoustic environment.
  arXiv  Detail & Related papers  (2023-03-07T14:31:55Z)
• 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)
• Towards Building Text-To-Speech Systems for the Next Billion Users [18.290165216270452]
  We evaluate the choice of acoustic models, vocoders, supplementary loss functions, training schedules, and speaker and language diversity for Dravidian and Indo-Aryan languages. We train and evaluate TTS models for 13 languages and find our models to significantly improve upon existing models in all languages as measured by mean opinion scores.
  arXiv  Detail & Related papers  (2022-11-17T13:59:34Z)
• 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)

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.