Hierarchical Latent Words Language Models for Automatic Speech Recognition

Focusing language models for automatic speech recognition

This paper describes a method for selecting text data from a corpus with the aim of training auxiliary Language Models (LMs) for an Automatic Speech Recognition (ASR) system. A novel similarity score function is proposed, which allows to score each document belonging to the corpus in order to select those with the highest scores for training auxiliary LMs which are linearly interpolated with th...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Towards the Application of Hierarchical Bayesian Models on Language Models for Automatic Speech Recognition

We introduce our ongoing work towards the application of hierarchical Bayesian models on language models for automatic speech recognition, in the context of multiparty meetings. We first present our framework, and then address some practical issues.

Language Models for Automatic Speech Recognition of Czech Lectures

This paper describes improvements in Automatic Speech Recognition (ASR) of Czech lectures obtained by enhancing language models. Our baseline is a statistical trigram language model with Good-Turing smoothing, trained on half billion words from newspapers, books etc. The overall improvement from adding more training data is over 10% in accuracy absolute, while using advanced language modeling t...