Statistical language modeling using a small corpus from an application domain

Abstract

Statistical language models have been successfully used to improve the performance of continuous speech recognition algorithms. Application of such techniques is difficult when only a small training corpus is available. The authors present an approach for dealing with limited training available from the DARPA resource management domain. An initial training corpus of sentences was abstracted by replacing sentence fragments or phrases with variables. This training corpus of phrase sequences was used to derive parameters of a Markov model. The probability of a word sequence is then decomposed into the probability of possible phrase sequences within each of the phrases. Initial results obtained on 150 utterances from six speakers in the DARPA database indicate that this language modeling technique has potential for improved recognition performance. Furthermore, this approach provides a framework for incorporating linguistic knowledge into statistical language models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>