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

In this paper, we present a survey on the application of recurrent neural networks to the task
of statistical language modeling. Although it has been shown that these models obtain good …

We show that an end-to-end deep learning approach can be used to recognize either
English or Mandarin Chinese speech–two vastly different languages. Because it replaces …

We present a state-of-the-art speech recognition system developed using end-to-end deep
learning. Our architecture is significantly simpler than traditional speech systems, which rely …

Audio-visual speech recognition (AVSR) system is thought to be one of the most promising
solutions for reliable speech recognition, particularly when the audio is corrupted by noise …

This chapter provides the background material and literature review for supervised
sequence labelling. Section 2.1 briefly reviews supervised learning in general. Section 2.2 …

We propose a novel context-dependent (CD) model for large-vocabulary speech recognition
(LVSR) that leverages recent advances in using deep belief networks for phone recognition …

Sequence-discriminative training of deep neural networks (DNNs) is investigated on a
standard 300 hour American English conversational telephone speech task. Different …

Recently, a new acoustic model based on deep neural networks (DNN) has been
introduced. While the DNN has generated significant improvements over GMM-based …

We apply the recently proposed Context-Dependent Deep-Neural-Network HMMs, or CD-
DNN-HMMs, to speech-to-text transcription. For single-pass speaker-independent …