This paper addresses text recognition for domains with limited manual annotations by a
simple self-training strategy. Our approach should reduce human annotation effort when …

Word vector representations are a crucial part of natural language processing (NLP) and
human computer interaction. In this paper, we propose a novel word vector representation …

Automatic speech recognition (ASR) systems often make unrecoverable errors due to
subsystem pruning (acoustic, language and pronunciation models); for example, pruning …

Discriminative language modeling aims to reduce the error rates by rescoring the output of
an automatic speech recognition (ASR) system. Discriminative language model (DLM) …

Training domain-specific automatic speech recognition (ASR) systems requires a suitable
amount of data comprising the target domain. In several scenarios, such as early …

Discriminative language modeling (DLM) has been shown to improve the accuracy of
automatic speech recognition (ASR) systems, but it requires large amounts of both acoustic …

This paper summarizes the research on discriminative language modeling focusing on its
application to automatic speech recognition (ASR). A discriminative language model (DLM) …

One way to improve the accuracy of automatic speech recognition (ASR) is to use
discriminative language modeling (DLM), which enhances discrimination by learning where …

Discriminative language modeling is a successful approach to improving speech recognition
accuracy. However, it requires a large amount of spoken data and manually transcribed …

In this paper, we describe the work developed in the creation of syllable-based language
model for continuous speech recognition system for Myanmar language. Speech recognition …