Abstract
In this paper, an attempt is made to explore the dynamics of speech prosody to characterize and classify emotional states in a speech signal. The local or fine variations describing the prosodic dynamics are combined with the static prosodic parameters for a possible enhancement in the emotional speech recognition (ESR) accuracy. The efficient vector quantization (VQ) clustering algorithm has been applied to compress the static and dynamic parameters before further processing in a radial basis neural network (RBFNN) platform. Results reveal an improvement in ESR accuracy of 86.05% by involving both static and dynamic prosodic features as compared to 84.92% accuracy when the combination of static prosodic feature simulated alone.
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Palo, H.K., Mohanty, M.N. (2020). Analysis of Speech Emotions Using Dynamics of Prosodic Parameters. In: Mallick, P., Balas, V., Bhoi, A., Chae, GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-15-1451-7_36
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DOI: https://doi.org/10.1007/978-981-15-1451-7_36
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