The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification

Anat Lerner; Oren Miara; Sarit Malayev; Vered Silber-Varod

doi:10.1007/978-3-319-99579-3_34

The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification

Anat Lerner, Oren Miara, Sarit Malayev, Vered Silber-Varod

Computer Science discipline

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The objective of the current on-going research is to automatically identify the role played by a speaker in a dialogue, and to explore potential conditions that might impose higher speaker’s role identification. We use an interactive Map Task setup with two potential roles: followers and leaders, where each speaker participated twice thus acting in both roles with the same interlocutor. The paper aims to identify speaker’s role, and to explore potential influence of the gender of the speaker, the gender of the interlocutor, and the order of the roles played by the speaker. By using deep learning procedures over a set of acoustic features, we automatically trace the footprints of the role through the speech signal. Results show an average of 73.3% role’s classification rate. We further show that there is a significant difference in the role’s classification rates, depending on the interlocutor’s gender. On average, when the interlocutor is a male, the speaker tends to identify with his or her role more clearly – 77.5% versus 69.9% when the interlocutor is a woman.

Original language	English
Title of host publication	Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings
Editors	Rodmonga Potapova, Oliver Jokisch, Alexey Karpov
Publisher	Springer Verlag
Pages	321-330
Number of pages	10
ISBN (Print)	9783319995786
DOIs	https://doi.org/10.1007/978-3-319-99579-3_34
State	Published - 2018
Event	20th International Conference on Speech and Computer, SPECOM 2018 - Leipzig, Germany Duration: 18 Sep 2018 → 22 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11096 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Speech and Computer, SPECOM 2018
Country/Territory	Germany
City	Leipzig
Period	18/09/18 → 22/09/18

Bibliographical note

Publisher Copyright:
© 2018, Springer Nature Switzerland AG.

Keywords

Acoustic features
Deep learning
Dialogues
Gender
Interlocutors
Map Task corpus
Role identification

Access to Document

10.1007/978-3-319-99579-3_34

Cite this

Lerner, A., Miara, O., Malayev, S., & Silber-Varod, V. (2018). The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification. In R. Potapova, O. Jokisch, & A. Karpov (Eds.), Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings (pp. 321-330). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11096 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-99579-3_34

Lerner, Anat ; Miara, Oren ; Malayev, Sarit et al. / The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification. Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings. editor / Rodmonga Potapova ; Oliver Jokisch ; Alexey Karpov. Springer Verlag, 2018. pp. 321-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "The Influence of the Interlocutor{\textquoteright}s Gender on the Speaker{\textquoteright}s Role Identification",

abstract = "The objective of the current on-going research is to automatically identify the role played by a speaker in a dialogue, and to explore potential conditions that might impose higher speaker{\textquoteright}s role identification. We use an interactive Map Task setup with two potential roles: followers and leaders, where each speaker participated twice thus acting in both roles with the same interlocutor. The paper aims to identify speaker{\textquoteright}s role, and to explore potential influence of the gender of the speaker, the gender of the interlocutor, and the order of the roles played by the speaker. By using deep learning procedures over a set of acoustic features, we automatically trace the footprints of the role through the speech signal. Results show an average of 73.3% role{\textquoteright}s classification rate. We further show that there is a significant difference in the role{\textquoteright}s classification rates, depending on the interlocutor{\textquoteright}s gender. On average, when the interlocutor is a male, the speaker tends to identify with his or her role more clearly – 77.5% versus 69.9% when the interlocutor is a woman.",

keywords = "Acoustic features, Deep learning, Dialogues, Gender, Interlocutors, Map Task corpus, Role identification",

author = "Anat Lerner and Oren Miara and Sarit Malayev and Vered Silber-Varod",

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Lerner, A, Miara, O, Malayev, S & Silber-Varod, V 2018, The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification. in R Potapova, O Jokisch & A Karpov (eds), Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11096 LNAI, Springer Verlag, pp. 321-330, 20th International Conference on Speech and Computer, SPECOM 2018, Leipzig, Germany, 18/09/18. https://doi.org/10.1007/978-3-319-99579-3_34

The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification. / Lerner, Anat; Miara, Oren; Malayev, Sarit et al.
Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings. ed. / Rodmonga Potapova; Oliver Jokisch; Alexey Karpov. Springer Verlag, 2018. p. 321-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11096 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Malayev, Sarit

AU - Silber-Varod, Vered

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AB - The objective of the current on-going research is to automatically identify the role played by a speaker in a dialogue, and to explore potential conditions that might impose higher speaker’s role identification. We use an interactive Map Task setup with two potential roles: followers and leaders, where each speaker participated twice thus acting in both roles with the same interlocutor. The paper aims to identify speaker’s role, and to explore potential influence of the gender of the speaker, the gender of the interlocutor, and the order of the roles played by the speaker. By using deep learning procedures over a set of acoustic features, we automatically trace the footprints of the role through the speech signal. Results show an average of 73.3% role’s classification rate. We further show that there is a significant difference in the role’s classification rates, depending on the interlocutor’s gender. On average, when the interlocutor is a male, the speaker tends to identify with his or her role more clearly – 77.5% versus 69.9% when the interlocutor is a woman.

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Lerner A, Miara O, Malayev S, Silber-Varod V. The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification. In Potapova R, Jokisch O, Karpov A, editors, Speech and Computer - 20th International Conference, SPECOM 2018, Proceedings. Springer Verlag. 2018. p. 321-330. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-99579-3_34

The Influence of the Interlocutor’s Gender on the Speaker’s Role Identification

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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