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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationSpeech and Computer - 20th International Conference, SPECOM 2018, Proceedings
EditorsRodmonga Potapova, Oliver Jokisch, Alexey Karpov
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319995786
StatePublished - 2018
Event20th International Conference on Speech and Computer, SPECOM 2018 - Leipzig, Germany
Duration: 18 Sep 201822 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11096 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Speech and Computer, SPECOM 2018

Bibliographical note

Publisher Copyright:
© 2018, Springer Nature Switzerland AG.


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


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