MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition
and Robust Speech-to-Text Translation
- URL: http://arxiv.org/abs/2303.00628v1
- Date: Wed, 1 Mar 2023 16:31:01 GMT
- Title: MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition
and Robust Speech-to-Text Translation
- Abstract summary: We introduce MuAViC, a multilingual audio-visual corpus for robust speech recognition and robust speech-to-text translation.
It is fully transcribed and covers 6 English-to-X translation as well as 6 X-to-English translation directions.
Our baseline results show that MuAViC is effective for building noise-robust speech recognition and translation models.
- Score: 44.53711548080296
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce MuAViC, a multilingual audio-visual corpus for robust speech
recognition and robust speech-to-text translation providing 1200 hours of
audio-visual speech in 9 languages. It is fully transcribed and covers 6
English-to-X translation as well as 6 X-to-English translation directions. To
the best of our knowledge, this is the first open benchmark for audio-visual
speech-to-text translation and the largest open benchmark for multilingual
audio-visual speech recognition. Our baseline results show that MuAViC is
effective for building noise-robust speech recognition and translation models.
We make the corpus available at https://github.com/facebookresearch/muavic.
Related papers
- XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception [62.660135152900615]
Speech recognition and translation systems perform poorly on noisy inputs.
XLAVS-R is a cross-lingual audio-visual speech representation model for noise-robust speech recognition and translation.
arXiv Detail & Related papers (2024-03-21T13:52:17Z) - AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation [58.72068260933836]
The input and output of the system are multimodal (i.e., audio and visual speech)
We can perform real-like conversations with individuals worldwide in a virtual meeting by utilizing our own primary languages.
In contrast to Speech-to-Speech Translation (A2A), which solely translates between audio modalities, the proposed AV2AV directly translates between audio-visual speech.
arXiv Detail & Related papers (2023-12-05T05:36:44Z) - SeamlessM4T: Massively Multilingual & Multimodal Machine Translation [90.71078166159295]
We introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-text translation, and automatic speech recognition for up to 100 languages.
We developed the first multilingual system capable of translating from and into English for both speech and text.
On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation.
arXiv Detail & Related papers (2023-08-22T17:44:18Z) - 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) - SpeechMatrix: A Large-Scale Mined Corpus of Multilingual
Speech-to-Speech Translations [38.058120432870126]
SpeechMatrix is a large-scale multilingual corpus of speech-to-speech translations.
It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech.
arXiv Detail & Related papers (2022-11-08T19:09:27Z) - The Multilingual TEDx Corpus for Speech Recognition and Translation [30.993199499048824]
We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages.
The corpus is a collection of audio recordings from TEDx talks in 8 source languages.
We segment transcripts into sentences and align them to the source-language audio and target-language translations.
arXiv Detail & Related papers (2021-02-02T21:16:25Z)
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.