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,
with approximately 12 hours of speech supervision per language. FLEURS can be
used for a variety of speech tasks, including Automatic Speech Recognition
(ASR), Speech Language Identification (Speech LangID), Translation and
Retrieval. In this paper, we provide baselines for the tasks based on
multilingual pre-trained models like mSLAM. The goal of FLEURS is to enable
speech technology in more languages and catalyze research in low-resource
speech understanding.

介绍 FLEURS，这是一个 Few-shot Learning Evaluation of Universal Representations of Speech 基准，包含 102 种语言的 n 方并行语音数据集，可用于自动语音识别 (ASR)，语音语言识别 (Speech LangID)，翻译和检索等语音任务，并基于多语言预训练模型提供基线，旨在推动语音技术在多种语言中的应用。

FLEURS: 语音通用表示的少样本学习评估

FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech

This paper presents XLSR which learns cross-lingual speech representations by
pretraining a single model from the raw waveform of speech in multiple
languages. We build on wav2vec 2.0 which is trained by solving a contrastive
task over masked latent speech representations and jointly learns a
quantization of the latents shared across languages. The resulting model is
fine-tuned on labeled data and experiments show that cross-lingual pretraining
significantly outperforms monolingual pretraining. On the CommonVoice
benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared
to the best known results. On BABEL, our approach improves word error rate by
16% relative compared to a comparable system. Our approach enables a single
multilingual speech recognition model which is competitive to strong individual
models. Analysis shows that the latent discrete speech representations are
shared across languages with increased sharing for related languages. We hope
to catalyze research in low-resource speech understanding by releasing XLSR-53,
a large model pretrained in 53 languages.

XLSR 是一种学习跨语言语音表示的模型，通过对多种语言的语音原始波形进行预训练来构建模型，上述模型建立在 wav2vec 2.0 的基础之上，可以使用单一模型完成多语言语音识别任务，并且具有更好的性能。