Self-supervised learning (SSL) has helped extend speech technologies to more
languages by reducing the need for labeled data. However, models are still far
from supporting the world's 7000+ languages. We propose XEUS, a Cross-lingual
Encoder for Universal Speech, trained on over 1 million hours of data across
4057 languages, extending the language coverage of SSL models 4-fold. We
combine 1 million hours of speech from existing publicly accessible corpora
with a newly created corpus of 7400+ hours from 4057 languages, which will be
publicly released. To handle the diverse conditions of multilingual speech
data, we augment the typical SSL masked prediction approach with a novel
dereverberation objective, increasing robustness. We evaluate XEUS on several
benchmarks, and show that it consistently outperforms or achieves comparable
results to state-of-the-art (SOTA) SSL models across a variety of tasks. XEUS
sets a new SOTA on the ML-SUPERB benchmark: it outperforms MMS 1B and w2v-BERT
2.0 v2 by 0.8% and 4.4% respectively, despite having less parameters or
pre-training data. Checkpoints, code, and data are found in
this https URL

我们提出了 XEUS，一种用于普适语音的跨语言编码器，通过对来自 4057 种语言的超过 1 百万小时数据进行训练，将 SSL 模型的语言覆盖范围提高了 4 倍，并在多项基准测试中展现出优于或与最先进的 SSL 模型的可比结果。

面向成千上万种语言的鲁棒语音表征学习

Towards Robust Speech Representation Learning for Thousands of Languages

Expression of emotions is a crucial part of daily human communication.
Emotion recognition in conversations (ERC) is an emerging field of study, where
the primary task is to identify the emotion behind each utterance in a
conversation. Though a lot of work has been done on ERC in the past, these
works only focus on ERC in the English language, thereby ignoring any other
languages. In this paper, we present Multilingual MELD (M-MELD), where we
extend the Multimodal EmotionLines Dataset (MELD) \cite{poria2018meld} to 4
other languages beyond English, namely Greek, Polish, French, and Spanish.
Beyond just establishing strong baselines for all of these 4 languages, we also
propose a novel architecture, DiscLSTM, that uses both sequential and
conversational discourse context in a conversational dialogue for ERC. Our
proposed approach is computationally efficient, can transfer across languages
using just a cross-lingual encoder, and achieves better performance than most
uni-modal text approaches in the literature on both MELD and M-MELD. We make
our data and code publicly on GitHub.

本研究提出 Multilingual MELD（M-MELD）数据集和一种使用序列和对话语境的 DiscLSTM 方法，旨在进行跨语言的情感识别，其计算效率高且表现优于现有文献中大多数单模态文本方法。