A Systematic Comparison of Different HMM Designs for Emotion Recognition from Acted and Spontaneous Speech

  • Johannes Wagner
  • Thurid Vogt
  • Elisabeth André
Conference paper

DOI: 10.1007/978-3-540-74889-2_11

Volume 4738 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Wagner J., Vogt T., André E. (2007) A Systematic Comparison of Different HMM Designs for Emotion Recognition from Acted and Spontaneous Speech. In: Paiva A.C.R., Prada R., Picard R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2007. Lecture Notes in Computer Science, vol 4738. Springer, Berlin, Heidelberg

Abstract

In this work we elaborate the use of hidden Markov models (HMMs) for speech emotion recognition as a dynamic alternative to static modelling approaches. Since previous work on this field does not yet define a clear line which HMM design should be prioritised for this task, we run a systematic analysis of different HMM configurations. Furthermore, experiments are carried out on an acted and a spontaneous emotions corpus, since little is known about the suitability of HMMs for spontaneous speech. Additionally, we consider two different segmentation levels, namely words and utterances. Results are compared with the outcome of a support vector machine classifier trained on global statistics features. While for both databases similar performance was observed on utterance level, the HMM-based approach outperformed static classification on word level. However, setting up general guidelines which kind of models are best suited appeared to be rather difficult.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Johannes Wagner
    • 1
  • Thurid Vogt
    • 1
  • Elisabeth André
    • 1
  1. 1.Multimedia concepts and applications, Augsburg UniversityGermany