Enhancing multilingual recognition of emotion in speech by language identification

Hesam Sagha, Pavel Matejka, Maryna Gavryukova, Filip Povolny, Erik Marchi, Björn Schuller

Research output: Contribution to journalConference articlepeer-review

28 Scopus citations

Abstract

We investigate, for the first time, if applying model selection based on automatic language identification (LID) can improve multilingual recognition of emotion in speech. Six emotional speech corpora from three language families (Germanic, Romance, Sino-Tibetan) are evaluated. The emotions are represented by the quadrants in the arousal/valence plane, i. e., positive/negative arousal/valence. Four selection approaches for choosing an optimal training set depending on the current language are compared: within the same language family, across language family, use of all available corpora, and selection based on the automatic LID. We found that, on average, the proposed LID approach for selecting training corpora is superior to using all the available corpora when the spoken language is not known.

Original languageEnglish
Pages (from-to)2949-2953
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume08-12-September-2016
DOIs
StatePublished - 2016
Externally publishedYes
Event17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 - San Francisco, United States
Duration: 8 Sep 201616 Sep 2016

Keywords

  • Language families
  • Language identification
  • Multilingual emotion recognition

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