The Relevance of Voice Quality Features in Speaker Independent Emotion Recognition

Venue

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. IV-17-IV-20

Publication Year

2007

Keywords

Signal processing,Bayes methods,Bayesian classifier,Bayesian methods,emotion recognition,Emotion recognition,Feature extraction,Mel frequency cepstral coefficient,Pattern classification,phonation types,Production,prosodic features,Psychology,Spatial databases,speaker independent,speaker independent emotion recognition,speaker recognition,Speech analysis,speech processing,voice quality features

Identifiers

Authors

  • Marko Lugger
  • Bin Yang

Abstract

This paper investigates the classification of different emotional states using presodic and voice quality information. We want to exploit the usage of different phonation types within the production of emotions. Therefore, as features we use prosodic features, voice quality parameters, and different combinations of both types. We study how prosodic and voice quality features overlap or complement each other in the application of emotion recognition. The classification is speaker independent and uses a reduced subset of 8 features and a Bayesian classifier.

Source Materials