Chapter

Affective Computing and Intelligent Interaction

Volume 6975 of the series Lecture Notes in Computer Science pp 407-414

Audio-Based Emotion Recognition from Natural Conversations Based on Co-Occurrence Matrix and Frequency Domain Energy Distribution Features

  • Aya SayedelahlAffiliated withPattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo
  • , Pouria FewzeeAffiliated withPattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo
  • , Mohamed S. KamelAffiliated withPattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo
  • , Fakhri KarrayAffiliated withPattern Analysis and Machine Intelligence Lab, Electrical and Computer Engineering, University of Waterloo

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Abstract

Emotion recognition from natural speech is a very challenging problem. The audio sub-challenge represents an initial step towards building an efficient audio-visual based emotion recognition system that can detect emotions for real life applications (i.e. human-machine interaction and/or communication). The SEMAINE database, which consists of emotionally colored conversations, is used as the benchmark database. This paper presents our emotion recognition system from speech information in terms of positive/negative valence, and high and low arousal, expectancy and power. We introduce a new set of features including Co-Occurrence matrix based features as well as frequency domain energy distribution based features. Comparisons between well-known prosodic and spectral features and the new features are presented. Classification using the proposed features has shown promising results compared to the classical features on both the development and test data sets.

Keywords

Speech Emotion Recognition Co-Occurrence Matrix Frequency Domain Energy Distribution