Recurrent neural network language models (RNNLMs) have recently become increasingly
popular for many applications including speech recognition. In previous research RNNLMs …

In recent years, recurrent neural network language models (RNNLMs) have become
increasingly popular for a range of applications including speech recognition. However, the …

In recent years recurrent neural network language models (RNNLMs) have been
successfully applied to a range of tasks including speech recognition. However, an …

We explore two adaptation approaches of deep Transformer based neural language models
(LMs) for automatic speech recognition. The first approach is a pretrain-finetune framework …

Recurrent neural network language models (RNNLMs) are becoming increasingly popular
for a range of applications including automatic speech recognition. An important issue that …

Although Long-Short Term Memory (LSTM) networks and deep Transformers are now
extensively used in offline ASR, it is unclear how best offline systems can be adapted to …

An important part of the language modelling problem for automatic speech recognition
(ASR) systems, and many other related applications, is to appropriately model long-distance …

This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system
using the proposed integrated features vector with Recurrent Neural Network (RNN) based …

Abstract Language Models (LMs) for Automatic Speech Recognition (ASR) are typically
trained on large text corpora from news articles, books and web documents. These types of …

Current state-of-the-art models based on Long-Short Term Memory (LSTM) networks have
been extensively used in ASR to improve performance. However, using LSTMs under a …