Contextual Information and Specific Language Models for Spoken Language Understanding

TL;DR

This paper demonstrates that using contextually tailored language models in spoken language understanding systems significantly improves recognition and understanding accuracy in task-oriented dialogues, especially in spontaneous telephone speech.

Contribution

It introduces a method for generating and applying context-specific language models based on dialogue context, enhancing spoken language understanding performance.

Findings

Recognition accuracy improved with specific language models

Understanding performance increased due to contextual modeling

Experimental results confirm the effectiveness of the approach

Abstract

In this paper we explain how contextual expectations are generated and used in the task-oriented spoken language understanding system Dialogos. The hard task of recognizing spontaneous speech on the telephone may greatly benefit from the use of specific language models during the recognition of callers' utterances. By 'specific language models' we mean a set of language models that are trained on contextually appropriated data, and that are used during different states of the dialogue on the basis of the information sent to the acoustic level by the dialogue management module. In this paper we describe how the specific language models are obtained on the basis of contextual information. The experimental result we report show that recognition and understanding performance are improved thanks to the use of specific language models.