Abstract
Worldwide research is going on to judge the emotional state of a speaker just from the quality of human voice. This paper explores use of supervised neural network to design a classifier that can discriminate between several emotions like happiness, anger, fear, sadness & unemotional state in speech. The results found to be are significant, both in cognitive science and in speech technology. In the current paper, statistics of the pitch like, first and second formants, and Energy and speaking rate are used as relevant features. Different neural network based recognizers are created. Ensembles of such recognizers are used as an important part of decision support system for prioritizing voice messages and assigning a proper agent to response the message. The developed intelligent system can be enhanced to automatically predict and adapt to detect people’s emotional states and also to design emotional robot or computer system.
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© 2004 Springer-Verlag Berlin Heidelberg
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Giripunje, S., Panat, A. (2004). Speech Recognition for Emotions with Neural Network: A Design Approach. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_84
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DOI: https://doi.org/10.1007/978-3-540-30133-2_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23206-3
Online ISBN: 978-3-540-30133-2
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