3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms

Noushin Hajarolasvadi · Hasan Demirel

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

Abstract

Detecting human intentions and emotions helps improve human–robot interactions. Emotion recognition has been a challenging research direction in the past decade. This paper proposes an emotion recognition system based on analysis of speech signals. Firstly, we split each speech signal into overlapping frames of the same length. Next, we extract an 88-dimensional vector of audio features including Mel Frequency Cepstral Coefficients (MFCC), pitch, and intensity for each of the respective frames. In parallel, the spectrogram of each frame is generated. In the final preprocessing step, by applying k-means clustering on the extracted features of all frames of each audio signal, we select k most discriminant frames, namely keyframes, to summarize the speech signal. Then, the sequence of the corresponding spectrograms of keyframes is encapsulated in a 3D tensor. These tensors are used to train and test a 3D Convolutional Neural network using a 10-fold cross-validation approach. The proposed 3D CNN has two convolutional layers and one fully connected layer. Experiments are conducted on the Surrey Audio-Visual Expressed Emotion (SAVEE), Ryerson Multimedia Laboratory (RML), and eNTERFACE’05 databases. The results are superior to the state-of-the-art methods reported in the literature.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2005A database of German emotional speech 2011Multimodal Emotion Recognition in Response to Videos 2006The eNTERFACE’05 Audio-Visual Emotion Database 2014Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks 2017Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms 2017Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network 2013Deep learning for robust feature generation in audiovisual emotion recognition

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2020Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques 2021Att-Net: Enhanced emotion recognition system using lightweight self-attention module 2022A systematic literature review of speech emotion recognition approaches 2020Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks 2021Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition

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