This article analyzes and compares influence of different types of spectral and prosodic features for Czech and\r\nSlovak emotional speech classification based on Gaussian mixture models (GMM). Influence of initial setting of\r\nparameters (number of mixture components and used number of iterations) for GMM training process was\r\nanalyzed, too. Subsequently, analysis was performed to find how correctness of emotion classification depends on\r\nthe number and the order of the parameters in the input feature vector and on the computation complexity.\r\nAnother test was carried out to verify the functionality of the proposed two-level architecture comprising the\r\ngender recognizer and of the emotional speech classifier. Next tests were realized to find dependence of some\r\nnegative aspect (processing of the input speech signal with too short time duration, the gender of a speaker\r\nincorrectly determined, etc.) on the stability of the results generated during the GMM classification process.\r\nEvaluations and tests were realized with the speech material in the form of sentences of male and female speakers\r\nexpressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. In\r\naddition, a comparative experiment using the speech data corpus in other language (German) was performed. The\r\nmean classification error rate of the whole classifier structure achieves about 21% for all four emotions and both\r\ngenders, and the best obtained error rate was 3.5% for the sadness style of the female gender. These values are\r\nacceptable in this first stage of development of the GMM classifier. On the other hand, the test showed the\r\nprincipal importance of correct classification of the speaker gender in the first level, which has heavy influence on\r\nthe resulting recognition score of the emotion classification. This GMM classifier should be used for evaluation of\r\nthe synthetic speech quality after applied voice conversion and emotional speech style transformation.
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