A Discriminative Acoustic-Prosodic Approach for Measuring Local Entrainment

TL;DR

This paper introduces a discriminative acoustic-prosodic method to measure local entrainment in conversations, which predicts conversational success with 72% accuracy, outperforming previous approaches.

Contribution

It proposes a novel turn-by-turn local entrainment measurement method based on discriminative features that distinguish real from sham conversations.

Findings

Achieved 72% classification accuracy in predicting conversational success.

Outperformed three previous methods on the same dataset.

Demonstrated the effectiveness of turn-by-turn acoustic-prosodic features.

Abstract

Acoustic-prosodic entrainment describes the tendency of humans to align or adapt their speech acoustics to each other in conversation. This alignment of spoken behavior has important implications for conversational success. However, modeling the subtle nature of entrainment in spoken dialogue continues to pose a challenge. In this paper, we propose a straightforward definition for local entrainment in the speech domain and operationalize an algorithm based on this: acoustic-prosodic features that capture entrainment should be maximally different between real conversations involving two partners and sham conversations generated by randomly mixing the speaking turns from the original two conversational partners. We propose an approach for measuring local entrainment that quantifies alignment of behavior on a turn-by-turn basis, projecting the differences between interlocutors' acoustic-prosodic features for a given turn onto a discriminative feature subspace that maximizes the difference between real and sham conversations. We evaluate the method using the derived features to drive a classifier aiming to predict an objective measure of conversational success (i.e., low versus high), on a corpus of task-oriented conversations. The proposed entrainment approach achieves 72% classification accuracy using a Naive Bayes classifier, outperforming three previously established approaches evaluated on the same conversational corpus.