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

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 …

There is a vast body of literature on LVCSR research and some limitation is necessary in the
scope of this article. We will focus primarily on the techniques that have been successful in …

In this paper, the improvements in the recently implemented Kannada speech recognition
system is demonstrated in detail. The Kannada automatic speech recognition (ASR) system …

A new type of deep neural networks (DNNs) is presented in this paper. Traditional DNNs
use the multinomial logistic regression (softmax activation) at the top layer for classification …

Modern automatic speech recognition systems handle large vocabularies of words, making
it infeasible to collect enough repetitions of each word to train individual word models …

Convolutional neural networks (CNNs) have demonstrated the state-of-the-art performances
on automatic speech recognition. Softmax activation function for prediction and minimizing …

Structured discriminative models are a flexible sequence classification approach that enable
a wide variety of features to be used. This paper describes a particular model in this …

Automatic speech recognition (ASR) systems classify structured sequence data, where the
label sequences (sentences) must be inferred from the observation sequences (the acoustic …

Speech recognition is a process of capturing and changing the speech into digitized sound
waves, from which it converts to basic language or phonemes, constructing words, and …