Work2019

An Enhanced Human Speech Emotion Recognition Using Hybrid of PRNN and KNN

59 citations0/0 files storedInternational Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON)

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

In a system comprising of human-machine interaction, the emotion recognition of speech has always been a wide area of research since the machines can never analyze the emotion of a speaker on its own. To recognize the speaker's emotion, numerous systems were developed and tested. In this research study, an enhanced human speech emotion recognition system using a hybrid of PRNN and KNN algorithms is designed. The six basic emotions like neutral, anger, happiness, sadness, surprise and fear over the speech emotions are classified and studied for their accuracy with other previously developed systems. The database for this study is taken as the emotional speech samples of numbers. A cascaded system of Mel Frequency Cepstral Coefficient (MFCC) and Gray Level Co-occurrence Matrix (GLCM) was used for feature extraction process along with a Wiener filter for filtering the noise in speech. Also, a hybrid of Pattern Recognition Neural Network (PRNN) and K-Nearest Neighbour (KNN) is used for prediction accuracy of outcomes. The outcomes are compared with previously developed recognition systems and better efficiency is observed.

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References

2010Survey on speech emotion recognition: Features, classification schemes, and databases 2018Speech emotion recognition using deep 1D & 2D CNN LSTM networks 2011Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge 2017Evaluating deep learning architectures for Speech Emotion Recognition 2012Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011 2000Emotion Recognition in Speech Using Neural Networks 2018Speech emotion recognition based on an improved brain emotion learning model 2009Emotion recognition from speech signals using new harmony features

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2024Speech Databases, Speech Features, and Classifiers in Speech Emotion Recognition: A Review

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