Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

This paper presents XLS-R, a large-scale model for cross-lingual speech representation
learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a …

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 …

There are several domains that own corresponding widely used feature extractors, such as
ResNet, BERT, and GPT-x. These models are usually pre-trained on large amounts of …

With the emergence of deep learning, the performance of automatic speech recognition
(ASR) systems has remarkably improved. Especially for resource-rich languages such as …

In this paper, we propose a simple yet effective framework for multilingual end-to-end
speech translation (ST), in which speech utterances in source languages are directly …

Audio-visual speech recognition (AVSR) has gained remarkable success for ameliorating
the noise-robustness of speech recognition. Mainstream methods focus on fusing audio and …

We report on adaptation of multilingual end-to-end speech recognition models trained on as
many as 100 languages. Our findings shed light on the relative importance of similarity …

This work applies a hierarchical transfer learning to implement deep neural network (DNN)-
based multilingual text-to-speech (TTS) for low-resource languages. DNN-based system …

We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech
representations in many languages. XTREME-S covers four task families: speech …