Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …

We introduce the Universal Speech Model (USM), a single large model that performs
automatic speech recognition (ASR) across 100+ languages. This is achieved by pre …

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 …

We employ a combination of recent developments in semi-supervised learning for automatic
speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled …

Recently, a semi-supervised learning method known as" noisy student training" has been
shown to improve image classification performance of deep networks significantly. Noisy …

Pseudo-labeling has recently shown promise in end-to-end automatic speech recognition
(ASR). We study Iterative Pseudo-Labeling (IPL), a semi-supervised algorithm which …

The Application of Hidden Markov Models in Speech Recognition Page 1 The Application of
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …

In this paper, we present an unsupervised learning framework to address the problem of
detecting spoken keywords. Without any transcription information, a Gaussian Mixture Model …

YouTube is a highly visited video sharing website where over one billion people watch six
billion hours of video every month. Improving accessibility to these videos for the hearing …