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 …

Self-supervised learning (SSL) achieves great success in speech recognition, while limited
exploration has been attempted for other speech processing tasks. As speech signal …

We summarize the results of a host of efforts using giant automatic speech recognition (ASR)
models pre-trained using large, diverse unlabeled datasets containing approximately a …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

We present SpeechStew, a speech recognition model that is trained on a combination of
various publicly available speech recognition datasets: AMI, Broadcast News, Common …

Recently, SpecAugment, an augmentation scheme for automatic speech recognition that
acts directly on the spectrogram of input utterances, has shown to be highly effective in …

End-to-end (E2E) models fold the acoustic, pronunciation and language models of a
conventional speech recognition model into one neural network with a much smaller …

All-neural end-to-end (E2E) automatic speech recognition (ASR) systems that use a single
neural network to transduce audio to word sequences have been shown to achieve state-of …

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as
quickly and accurately as possible. However, emitting fast without degrading quality, as …

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as
quickly and accurately as possible, while full-context ASR waits for the completion of a full …