A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition

Mustaqeem Mustaqeem · Soonil Kwon

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

Abstract

Speech is the most significant mode of communication among human beings and a potential method for human-computer interaction (HCI) by using a microphone sensor. Quantifiable emotion recognition using these sensors from speech signals is an emerging area of research in HCI, which applies to multiple applications such as human-reboot interaction, virtual reality, behavior assessment, healthcare, and emergency call centers to determine the speaker's emotional state from an individual's speech. In this paper, we present major contributions for; (i) increasing the accuracy of speech emotion recognition (SER) compared to state of the art and (ii) reducing the computational complexity of the presented SER model. We propose an artificial intelligence-assisted deep stride convolutional neural network (DSCNN) architecture using the plain nets strategy to learn salient and discriminative features from spectrogram of speech signals that are enhanced in prior steps to perform better. Local hidden patterns are learned in convolutional layers with special strides to down-sample the feature maps rather than pooling layer and global discriminative features are learned in fully connected layers. A SoftMax classifier is used for the classification of emotions in speech. The proposed technique is evaluated on Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets to improve accuracy by 7.85% and 4.5%, respectively, with the model size reduced by 34.5 MB. It proves the effectiveness and significance of the proposed SER technique and reveals its applicability in real-world applications.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2018The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English 2016Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network 2014Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks 2017Evaluating deep learning architectures for Speech Emotion Recognition 20183-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition 2017Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching

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2021A Comprehensive Review of Speech Emotion Recognition Systems 2021A Review on Speech Emotion Recognition Using Deep Learning and Attention Mechanism 2020MLT-DNet: Speech emotion recognition using 1D dilated CNN based on multi-learning trick approach 2023An ensemble 1D-CNN-LSTM-GRU model with data augmentation for speech emotion recognition 2022Robust Speech Emotion Recognition Using CNN+LSTM Based on Stochastic Fractal Search Optimization Algorithm

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