Lightweight Deep Learning Framework for Speech Emotion Recognition

doi:10.1109/ACCESS.2023.3297269

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Lightweight Deep Learning Framework for Speech Emotion Recognition

2023-01-01

Samson Akinpelu

Serestina Viriri *

Adekanmi Adeyinka Adegun

DOI：10.1109/ACCESS.2023.3297269

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Abstract

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Speech Emotion Recognition (SER) system, which analyzes human utterances to determine a speaker's emotion, has a growing impact on how people and machines interact. Recent growth in human-computer interaction and computational intelligence has drawn the attention of many researchers in Artificial Intelligence (AI) to deep learning because of its wider applicability to several fields, including computer vision, natural language processing, and affective computing, among others. Deep learning models do not need any form of manually created features because they can automatically extract the prospective features from the input data. Deep learning models, however, call for a lot of resources, high processing power, and hyper-parameter tuning, making them unsuitable for lightweight devices. In this study, we focused on developing an efficient lightweight model for speech emotion recognition with optimized parameters without compromising performance. Our proposed model integrates Random Forest and Multilayer Perceptron(MLP) classifiers into the VGGNet framework for efficient speech emotion recognition. The proposed model was evaluated against other deep learning based methods (InceptionV3, ResNet, MobileNetV2, DenseNet) and it yielded low computational complexity with optimum performance. The experiment was carried out on three datasets of TESS, EMODB, and RAVDESS, and Mel Frequency Cepstral Coefficient(MFCC) features were extracted with 6-8 variants of emotions namely, Sad, Angry, Happy, Surprise, Neutral, Disgust, Fear, and Calm. Our model demonstrated high performance of 100%, 96%, and 86.25% accuracy on TESS, EMODB, and RAVDESS datasets respectively. This revealed that the proposed lightweight model achieved higher accuracy of recognition compared to the recent state-of-the-art model found in the literature.

Keywords:

~Deep learning

convolutional neural network

speech emotion

lightweight

humancomputer interaction