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

By Mustaqeem, Soonil Kwon. Italian National Conference on Sensors, 2019

339 citations recorded by the source corpus.

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.

Subfields: Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/406c689128de141d49b11f6b2c35c7a51e8fd730

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In Experimental and Cognitive Psychology

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Citations in the corpus

A Comprehensive Review of Speech Emotion Recognition Systems (2021, methodology): “A different framework of CNN, referred to as Deep Stride Convolutional Neural Network (DSCNN), using strides in place of pooling layers, has been implemented in [97], [103] for emotion recognition.”
Emotion recognition and artificial intelligence: A systematic review (2014-2023) and research recommendations (2023, background): “Statistical features have provided a discriminant representation of speech signals, due to which it has obtained the effective classification of emotions [103,109,113,114].”
Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM (2020, methodology): “The recognition accuracy of the proposed system outperforms other CNN models that clearly indicated the robustness and signiﬁcance of the model for SER using spectrograms of speech signals [3].”