# Assessing User Expertise in Spoken Dialog System Interactions

## TL;DR

This paper proposes a method to automatically identify user expertise levels in spoken dialog systems using task-related features and machine learning classifiers, aiming to improve interaction adaptation and offline analysis.

## Contribution

It introduces a novel approach utilizing specific features and classifiers for expertise detection in dialog interactions, with preliminary results on real system data.

## Key findings

- Random Forests achieved promising classification accuracy.
- Support Vector Machine provided comparable results.
- Preliminary results indicate feasibility despite task difficulty.

## Abstract

Identifying the level of expertise of its users is important for a system since it can lead to a better interaction through adaptation techniques. Furthermore, this information can be used in offline processes of root cause analysis. However, not much effort has been put into automatically identifying the level of expertise of an user, especially in dialog-based interactions. In this paper we present an approach based on a specific set of task related features. Based on the distribution of the features among the two classes - Novice and Expert - we used Random Forests as a classification approach. Furthermore, we used a Support Vector Machine classifier, in order to perform a result comparison. By applying these approaches on data from a real system, Let's Go, we obtained preliminary results that we consider positive, given the difficulty of the task and the lack of competing approaches for comparison.