Recognition of Emotions in Speech Using Convolutional Neural Networks on Different Datasets

Marta Zielonka · Artur Piastowski · Andrzej Czyżewski · Paweł Nadachowski · Maksymilian Operlejn · Kamil Kaczor

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

Abstract

Artificial Neural Network (ANN) models, specifically Convolutional Neural Networks (CNN), were applied to extract emotions based on spectrograms and mel-spectrograms. This study uses spectrograms and mel-spectrograms to investigate which feature extraction method better represents emotions and how big the differences in efficiency are in this context. The conducted studies demonstrated that mel-spectrograms are a better-suited data type for training CNN-based speech emotion recognition (SER). The research experiments employed five popular datasets: Crowd-sourced Emotional Multimodal Actors Dataset (CREMA-D), Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAVEE), Toronto Emotional Speech Set (TESS), and The Interactive Emotional Dyadic Motion Capture (IEMOCAP). Six different classes of emotions were used: happiness, anger, sadness, fear, disgust, and neutral. However, some experiments were prepared to recognize just four emotions due to the characteristics of the IEMOCAP dataset. A comparison of classification efficiency on different datasets and an attempt to develop a universal model trained using all datasets were also performed. This approach brought an accuracy of 55.89% when recognizing four emotions. The most accurate model for six emotion recognition was trained and achieved 57.42% accuracy on a combination of four datasets (CREMA-D, RAVDESS, SAVEE, TESS). What is more, another study was developed that demonstrated that improper data division for training and test sets significantly influences the test accuracy of CNNs. Therefore, the problem of inappropriate data division between the training and test sets, which affected the results of studies known from the literature, was addressed extensively. The performed experiments employed the popular ResNet18 architecture to demonstrate the reliability of the research results and to show that these problems are not unique to the custom CNN architecture proposed in experiments. Subsequently, the label correctness of the CREMA-D dataset was studied through the employment of a prepared questionnaire.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2018The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English 2018Speech emotion recognition using deep 1D & 2D CNN LSTM networks 2014CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset 2016Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network 2019Speech Emotion Recognition Using Deep Learning Techniques: A Review 2017Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network

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