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Speech Emotion Recognition using Time Distributed 2D-Convolution layers for CAPSULENETS
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DOI：10.1007/s11042-022-12112-x.png)
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摘要
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Speech Emotion Recognition (SER) determines human emotions using linguistic and nonlinguistic features of the uttered speech. The nonlinguistic process is more suitable for applications where language is not a concern. In this paper, Capsule Network (CapsuleNets) with a combination of Time Distributed 2D-Convolution layers is used for classifying emotions using speech signals. CapsuleNets are specially designed to capture the spatial cues of the data but fail in considering temporal cues in time series data like speech. In order to capture the temporal cues, along with spatial cues, Time distributed 2D- convolution neural layers are introduced before the CapsuleNets. Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) speech data sets are used for experimenting with the proposed network architecture. The log-mel spectrogram of the speech samples is extracted and used for training and testing of the proposed model. The combination of CapsuleNets with Time Distributed 2D-Convolution layers has achieved a classification accuracy of 92.6% on the RAVDESS dataset and 93.2% on the IEMOCAP dataset. These results are compared with the plain CapsuleNets model, and remarkable improvement is observed. Also, the proposed system has outperformed the existing models on the mentioned benchmarked datasets. The confusion matrix shows consistent improvement in the accuracy of every emotion, including sad and disgust in RAVDESS and angry in IEMOCAP, which are poorly classified by classifiers such as variants in CNN, RNN, LSTM.
Keyword:
Speech Emotion Recognition (SER)
Spectrogram
Time distributed layers
CapsuleNets
Deep Neural Networks (DNN)
RAVDESS
And IEMOCAP
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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