A historical perspective of speech recognition Page 1 review articles 94 communIcaTIonS of
The acm | jANuARY 2014 | vol. 57 | No. 1 WItH tHe IntroDUctIon of Apple’s Siri and similar …

Over the past few decades, there has been tremendous development in machine learning
paradigms used in automatic speech recognition (ASR) for home automation to space …

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 …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

We present Listen, Attend and Spell (LAS), a neural network that learns to transcribe speech
utterances to characters. Unlike traditional DNN-HMM models, this model learns all the …

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 …

This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a
time-synchronous encoder-decoder model that preserves the modularity of conventional …

Connectionist Speech Recognition: A Hybrid Approach describes the theory and
implementation of a method to incorporate neural network approaches into state of the art …

This paper presents an application of recurrent networks for phone probability estimation in
large vocabulary speech recognition. The need for efficient exploitation of context …

Abstract The use of Deep Belief Networks (DBN) to pretrain Neural Networks has recently
led to a resurgence in the use of Artificial Neural Network-Hidden Markov Model …