FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks
- URL: http://arxiv.org/abs/2408.06227v1
- Date: Mon, 12 Aug 2024 15:28:51 GMT
- Title: FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks
- Authors: Min Ma, Yuma Koizumi, Shigeki Karita, Heiga Zen, Jason Riesa, Haruko Ishikawa, Michiel Bacchiani,
- Abstract summary: FLEURS-R is a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech corpus.
The aim of FLEURS-R is to advance speech technology in more languages and catalyze research including text-to-speech.
- Score: 27.894172151026044
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces FLEURS-R, a speech restoration applied version of the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) corpus. FLEURS-R maintains an N-way parallel speech corpus in 102 languages as FLEURS, with improved audio quality and fidelity by applying the speech restoration model Miipher. The aim of FLEURS-R is to advance speech technology in more languages and catalyze research including text-to-speech (TTS) and other speech generation tasks in low-resource languages. Comprehensive evaluations with the restored speech and TTS baseline models trained from the new corpus show that the new corpus obtained significantly improved speech quality while maintaining the semantic contents of the speech. The corpus is publicly released via Hugging Face.
Related papers
- Recent Advances in Speech Language Models: A Survey [45.968078636811356]
Speech Language Models (SpeechLMs) are end-to-end models that generate speech without converting from text.
This paper provides the first comprehensive overview of recent methodologies for constructing SpeechLMs.
arXiv Detail & Related papers (2024-10-01T21:48:12Z) - Towards Unsupervised Speech Recognition Without Pronunciation Models [57.222729245842054]
Most languages lack sufficient paired speech and text data to effectively train automatic speech recognition systems.
We propose the removal of reliance on a phoneme lexicon to develop unsupervised ASR systems.
We experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling.
arXiv Detail & Related papers (2024-06-12T16:30:58Z) - Toward Joint Language Modeling for Speech Units and Text [89.32163954508489]
We explore joint language modeling for speech units and text.
We introduce automatic metrics to evaluate how well the joint LM mixes speech and text.
Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks.
arXiv Detail & Related papers (2023-10-12T20:53:39Z) - SpeechAlign: a Framework for Speech Translation Alignment Evaluation [15.069228503777124]
SpeechAlign is a framework designed to evaluate the underexplored field of source-target alignment in speech models.
To tackle the absence of suitable evaluation datasets, we introduce the Speech Gold Alignment dataset.
We also introduce two novel metrics, Speech Alignment Error Rate (SAER) and Time-weighted Speech Alignment Error Rate (TW-SAER)
arXiv Detail & Related papers (2023-09-20T18:46:37Z) - DisfluencyFixer: A tool to enhance Language Learning through Speech To
Speech Disfluency Correction [50.51901599433536]
DisfluencyFixer is a tool that performs speech-to-speech disfluency correction in English and Hindi.
Our proposed system removes disfluencies from input speech and returns fluent speech as output.
arXiv Detail & Related papers (2023-05-26T14:13:38Z) - Miipher: A Robust Speech Restoration Model Integrating Self-Supervised
Speech and Text Representations [51.89856133895233]
Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones.
In this study, we propose a robust SR model called Miipher, and apply Miipher to a new SR application.
To make our SR model robust against various degradation, we use (i) a speech representation extracted from w2v-BERT for the input feature, and (ii) a text representation extracted from transcripts via PnG-BERT as a linguistic conditioning feature.
arXiv Detail & Related papers (2023-03-03T01:57:16Z) - BASPRO: a balanced script producer for speech corpus collection based on
the genetic algorithm [29.701197643765674]
The performance of speech-processing models is heavily influenced by the speech corpus that is used for training and evaluation.
We propose BAlanced Script PROducer (BASPRO) system, which can automatically construct a phonetically balanced and rich set of Chinese sentences.
arXiv Detail & Related papers (2022-12-11T02:05:30Z) - FLEURS: Few-shot Learning Evaluation of Universal Representations of
Speech [33.71744518887916]
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark.
FLEURS is an n-way parallel speech dataset in 102 languages built on top of the machine translation FLoRes-101 benchmark.
arXiv Detail & Related papers (2022-05-25T02:29:03Z) - Towards Language Modelling in the Speech Domain Using Sub-word
Linguistic Units [56.52704348773307]
We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes.
With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech.
We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
arXiv Detail & Related papers (2021-10-31T22:48:30Z) - 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) - FT Speech: Danish Parliament Speech Corpus [21.190182627955817]
This paper introduces FT Speech, a new speech corpus created from the recorded meetings of the Danish Parliament.
The corpus contains over 1,800 hours of transcribed speech by a total of 434 speakers.
It is significantly larger in duration, vocabulary, and amount of spontaneous speech than the existing public speech corpora for Danish.
arXiv Detail & Related papers (2020-05-25T19:51:18Z)
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.