Emotions play an important role in human communication, and accurately recognising them from speech signals is critical for a variety of applications such as human-computer interaction and sentiment analysis. This study examines the effect of changing model parameters on the accuracy of speech emotion recognition systems. The research looks into the effects of changing key parameters. The results of extensive experiments on benchmark datasets shed light on the interdependence of different parameter settings and their influence on the accuracy of speech emotion recognition. The findings of this study contribute to a better understanding of optimal parameter selection and provide useful insights for improving the design and performance of speech emotion recognition systems.
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