TY - JOUR
T1 - A discriminative acoustic-prosodic approach for measuring local entrainment
AU - Willi, Megan M.
AU - Borrie, Stephanie A.
AU - Barrett, Tyson S.
AU - Tu, Ming
AU - Berisha, Visar
N1 - Funding Information:
This research was supported by the National Institute of Deafness and Other Communication Disorders, National Institutes of Health Grants R21DC016084-01 and R01DC006859.
Publisher Copyright:
© 2018 International Speech Communication Association. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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 opera-tionalize 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.
AB - 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 opera-tionalize 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.
KW - Conversational Success
KW - Entrainment
KW - Linear Discriminant Analysis
KW - Spoken Dialogue Systems
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U2 - 10.21437/Interspeech.2018-1419
DO - 10.21437/Interspeech.2018-1419
M3 - Conference article
AN - SCOPUS:85054993468
SN - 2308-457X
VL - 2018-September
SP - 581
EP - 585
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
T2 - 19th Annual Conference of the International Speech Communication, INTERSPEECH 2018
Y2 - 2 September 2018 through 6 September 2018
ER -