Automatic detection of real life negative emotions in speech has been evaluated using Linear Discriminant Analysis, LDA, with "classic" emotion features and a classifier based on Gaussian Mixture Models, GMMs. The latter uses Mel-Frequency Cepstral Coefficients, MFCCs, from a filter bank covering the 300.3400 Hz region to capture spectral shape and formants, and another in the 20.600 Hz region to capture prosody. Both classifiers have been tested on an extensive corpus from Swedish voice controlled telephone services. The results indicate that it is possible to detect anger with reasonable accuracy (average recall 83%) in natural speech and that the GMM method performed better than the LDA one.
Bibliographic reference. Neiberg, Daniel / Elenius, Kjell (2008): "Automatic recognition of anger in spontaneous speech", In INTERSPEECH-2008, 2755-2758.