Hidden Markov model-based speech emotion recognition

We introduce speech emotion recognition by use of continuous hidden Markov models. Two methods are propagated and compared. In the first method, a global statistics framework of an utterance is classified by Gaussian mixture models using derived features of the raw pitch and energy contour of the speech signal. A second method introduces increased temporal complexity, applying continuous hidden Markov models considering several states using low-level instantaneous features instead of global statistics. The paper addresses the design of working recognition engines, and results are achieved with respect to the alluded alternatives. A speech corpus consisting of acted and spontaneous emotion samples in German and English is described in detail. Both engines have been tested and trained using this equivalent speech corpus. Results in recognition of seven discrete emotions exceeded 86% recognition rate. In comparison, the judgment of human deciders classifying the same corpus at 79.8% recognition rate was analyzed.