Active learning by sparse instance tracking and classifier confidence in acoustic emotion recognition

Zixing Zhang, Björn Schuller

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

34 Scopus citations

Abstract

Data scarcity is an ever crucial problem in the field of acoustic emotion recognition. How to get the most informative data from a huge amount of data by least human work and at the same time to obtain the highest performance is quite important. In this paper, we propose and investigate two active learning strategies in acoustic emotion recognition: Based on sparse instances or based on classifier confidence scores. The first strategy focuses on the problem of unbalanced binary or multiple classes. The latter strategy pays more attention on clearing up the boundary confusion between different classes. Our experimental results show that by using active learning aiming at sparse instances or based on classifier confidence, the amount of transcribed data needed is significantly reduced and the unweigted accuracy boosts greatly as well.

Original languageEnglish
Title of host publication13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Pages362-365
Number of pages4
StatePublished - 2012
Event13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012 - Portland, OR, United States
Duration: 9 Sep 201213 Sep 2012

Publication series

Name13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Volume1

Conference

Conference13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
Country/TerritoryUnited States
CityPortland, OR
Period9/09/1213/09/12

Keywords

  • Acoustic emotion recognition
  • Active learning
  • Confidence scores
  • Sparse instances

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