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Affective Model Based Speech Emotion Recognition Using Deep Learning Techniques


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1 Department of IT, PSG College of Technology, Coimbatore 641 004, Tamil Nadu, India

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Human beings express emotions in multiple ways. Some common ways that emotions are expressed are through writing, speech, facial expression, body language or gesture. In general, it is believed that emotions are, first and foremost, internal feelings and experience. Speech is a powerful form of communication that is accompanied by the speaker's emotions. Specific prosodic signs, such as pitch variation, frequency, speech speed, rhythm, and voice quality, are accessible to speakers to express and listeners to interpret and decode the full spoken message. This paper aims to establish an affective model based speech emotion recognition system using deep learning techniques such as RNNwith LSTMon German and English Language datasets.

Keywords

Emotion recognition, RNN, Speech, Neural Network.
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  • Affective Model Based Speech Emotion Recognition Using Deep Learning Techniques

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Authors

D. Karthika Renuka
Department of IT, PSG College of Technology, Coimbatore 641 004, Tamil Nadu, India
C. Akalya Devi
Department of IT, PSG College of Technology, Coimbatore 641 004, Tamil Nadu, India
R. Kiruba Tharani
Department of IT, PSG College of Technology, Coimbatore 641 004, Tamil Nadu, India
G. Pooventhiran
Department of IT, PSG College of Technology, Coimbatore 641 004, Tamil Nadu, India

Abstract


Human beings express emotions in multiple ways. Some common ways that emotions are expressed are through writing, speech, facial expression, body language or gesture. In general, it is believed that emotions are, first and foremost, internal feelings and experience. Speech is a powerful form of communication that is accompanied by the speaker's emotions. Specific prosodic signs, such as pitch variation, frequency, speech speed, rhythm, and voice quality, are accessible to speakers to express and listeners to interpret and decode the full spoken message. This paper aims to establish an affective model based speech emotion recognition system using deep learning techniques such as RNNwith LSTMon German and English Language datasets.

Keywords


Emotion recognition, RNN, Speech, Neural Network.

References





DOI: https://doi.org/10.17010/ijcs%2F2020%2Fv5%2Fi4-5%2F154783