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 …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Expanding the language coverage of speech technology has the potential to improve
access to information for many more people. However, current speech technology is …

We study the capabilities of speech processing systems trained simply to predict large
amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual …

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 …

Recently great strides have been made in the field of automatic speech recognition (ASR) by
using various deep learning techniques. In this study, we present a thorough comparison …

This work dedicates to continuous sign language recognition (CSLR), which is a weakly
supervised task dealing with the recognition of continuous signs from videos, without any …

We study training a single acoustic model for multiple languages with the aim of improving
automatic speech recognition (ASR) performance on low-resource languages, and over-all …

Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic
speech recognition (ASR) coverage of the world's languages. They have shown …

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep
learning research, with a particular focus towards sequence-to-sequence models. Lingvo …