Lattice-rescoring is a common approach to take advantage of recurrent neural language
models in ASR, where a word-lattice is generated from 1st-pass decoding and the lattice is …

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 …

Abstract Recurrent Neural Networks, in particular Long-Short Term Memory (LSTM)
networks, are widely used in Automatic Speech Recognition for language modelling during …

This paper proposes a parallel computation strategy and a posterior-based lattice expansion
algorithm for efficient lattice rescoring with neural language models (LMs) for automatic …

Spoken 'grammatical error correction'(SGEC) is an important process to provide feedback for
second language learning. Due to a lack of end-to-end training data, SGEC is often …

Abstract Language Models (LMs) and Knowledge Graphs (KGs) are both active research
areas in Machine Learning and Semantic Web. While LMs have brought great …

The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates
an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) …

Language modeling is a crucial component in a wide range of applications including speech
recognition. Language models (LMs) are usually constructed by splitting a sentence into …

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 …

Computer assisted language learning (CALL) systems aidlearners to monitor their progress
by providing scoring andfeedback on language assessment tasks. Free speaking tests al …