Automatic speaker’s role classification with a bottom-up acoustic feature selection

Vered Silber-Varod, Anat Lerner, Oliver Jokisch

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


The objective of the current study is to automatically identify
the role played by the speaker in a dialogue. By using machine
learning procedures over acoustic feature, we wish to
automatically trace the footprints of this information through
the speech signal. The acoustic feature set was selected from a large statistic-based feature sets including 1,583 dimension features. The analysis is carried out on interactive dialogues of a Map Task setting. The paper first describes the methodology of choosing the 100 most effective attributes among the 1,583 features that were extracted, and then presents the classification results test of the same speaker in two different roles, and a gender-based classification. Results show an average of a 71% classification rate of the role the same speaker played, 65% for all women together and 65% for all men together.
Original languageAmerican English
Title of host publicationProceedings of the 2017 International Workshop on Grounding Language Understanding
Number of pages5
StatePublished - 2017
EventGrounding Language Understanding - KTH Royal Institute of Technology, Stockholm, Sweden
Duration: 25 Aug 201725 Aug 2017


ConferenceGrounding Language Understanding
Abbreviated titleGLU2017


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