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 …

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 …

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

Inspired by progress in large-scale language modeling, we apply a similar approach
towards building a single generalist agent beyond the realm of text outputs. The agent …

This paper explores a simple method for improving the zero-shot learning abilities of
language models. We show that instruction tuning--finetuning language models on a …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN)
dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and …

Recent progress in self-training, self-supervised pretraining and unsupervised learning
enabled well performing speech recognition systems without any labeled data. However, in …

Recently, there is increasing interest in multilingual automatic speech recognition (ASR)
where a speech recognition system caters to multiple low resource languages by taking …