Open AccessA Hybrid Technique using CNN LSTM for Speech Emotion Recognition
Author(s) -
Hafsa Qazi,
Baij Nath Kaushik
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e1027.069520
Subject(s) - spectrogram , computer science , speech recognition , emotion recognition , convolutional neural network , feature (linguistics) , artificial intelligence , feature extraction , pattern recognition (psychology) , philosophy , linguistics
Automatic speech emotion recognition is a very necessary activity for effective human-computer interaction. This paper is motivated by using spectrograms as inputs to the hybrid deep convolutional LSTM for speech emotion recognition. In this study, we trained our proposed model using four convolutional layers for high-level feature extraction from input spectrograms, LSTM layer for accumulating long-term dependencies and finally two dense layers. Experimental results on the SAVEE database shows promising performance. Our proposed model is highly capable as it obtained an accuracy of 94.26%.