Identifying Discourse Markers in Spoken Dialog

TL;DR

This paper introduces a machine learning method that uses POS tagging to identify discourse markers in spontaneous speech, enhancing speech recognition and dialog act prediction.

Contribution

It proposes a novel approach integrating POS tagging into language modeling for discourse marker detection in speech recognition systems.

Findings

Discourse markers can be identified using POS tags during speech recognition.

Incorporating discourse markers improves dialog act prediction.

The method outperforms previous approaches in identifying discourse markers.

Abstract

In this paper, we present a method for identifying discourse marker usage in spontaneous speech based on machine learning. Discourse markers are denoted by special POS tags, and thus the process of POS tagging can be used to identify discourse markers. By incorporating POS tagging into language modeling, discourse markers can be identified during speech recognition, in which the timeliness of the information can be used to help predict the following words. We contrast this approach with an alternative machine learning approach proposed by Litman (1996). This paper also argues that discourse markers can be used to help the hearer predict the role that the upcoming utterance plays in the dialog. Thus discourse markers should provide valuable evidence for automatic dialog act prediction.