Learning new acoustic events in an HMM-based system using MAP adaptation

Jürgen T. Geiger, Mohamed Anouar Lakhal, Björn Schuller, Gerhard Rigoll

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

In this paper, we present a system for the recognition of acoustic events suited for a robotic application. HMMs are used to model different acoustic event classes. We are especially looking at the open-set case, where a class of acoustic events occurs that was not included in the training phase. It is evaluated how newly occuring classes can be learnt using MAP adaptation or conventional training methods. A small database of acoustic events was recorded with a robotic platform to perform the experiments.

Original languageEnglish
Pages (from-to)293-296
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 2011
Event12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
Duration: 27 Aug 201131 Aug 2011

Keywords

  • Acoustic event classification
  • Hidden Markov models
  • Model adaptation

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