  Conventional feature-based classification methods do not apply well to
automatic recognition of speech emotions, mostly because the precise set of
spectral and prosodic features that is required to identify the emotional state
of a speaker has not been determined yet. This paper presents a method that
operates directly on the speech signal, thus avoiding the problematic step of
feature extraction. Furthermore, this method combines the strengths of the
classical source-filter model of human speech production with those of the
recently introduced liquid state machine (LSM), a biologically-inspired spiking
neural network (SNN). The source and vocal tract components of the speech
signal are first separated and converted into perceptually relevant spectral
representations. These representations are then processed separately by two
reservoirs of neurons. The output of each reservoir is reduced in
dimensionality and fed to a final classifier. This method is shown to provide
very good classification performance on the Berlin Database of Emotional Speech
(Emo-DB). This seems a very promising framework for solving efficiently many
other problems in speech processing.
