In search of the role’s footprints in client-therapist dialogues

Anat Lerner, Vered Silber-Varod, Fernando Batista, Helena Moniz

Research output: Contribution to journalConference articlepeer-review


The goal of this research is to identify speaker’s role via machine learning of broad acoustic parameters, in order to understand how an occupation, or a role, affects voice characteristics. The examined corpus consists of recordings taken under the same psychological paradigm (Process Work). Four interns were involved in four genuine client-therapist treatment sessions, where each individual had to train her therapeutic skills on her colleague that, in her turn, participated as a client. This uniform setting provided a unique opportunity to examine how role affects speaker’s prosody. By a collection of machine learning algorithms, we tested automatic classification of the role across sessions. Results based on the acoustic properties show high classification rates, suggesting that there are discriminative acoustic features of speaker’s role, as either a therapist or a client.

Original languageEnglish
Pages (from-to)400-404
Number of pages5
JournalProceedings of the International Conference on Speech Prosody
StatePublished - 2016
Event8th Speech Prosody 2016 - Boston, United States
Duration: 31 May 20163 Jun 2016

Bibliographical note

Publisher Copyright:
© 2016, International Speech Communications Association. All rights reserved.


  • Acoustic features
  • Client-therapist dialogue
  • Machine learning
  • Role identification
  • Speech analysis


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