DeepCNN: Spectro‐temporal feature representation for speech emotion recognition

Nasir Saleem · Jiechao Gao · Rizwana Irfan · Ahmad S. Almadhor · Hafiz Tayyab Rauf · Yudong Zhang +1 more

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

Abstract

Abstract Speech emotion recognition (SER) is an important research problem in human‐computer interaction systems. The representation and extraction of features are significant challenges in SER systems. Despite the promising results of recent studies, they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive fields. To mitigate this problem, this article proposes DeepCNN, which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks (CNNs) and a convolution layer‐based transformer. Two parallel CNNs are applied to extract the spectral features (2D‐CNN) and temporal features (1D‐CNN) representations. A 2D‐convolution layer‐based transformer module extracts spectro‐temporal features and concatenates them with features from parallel CNNs. The learnt low‐level concatenated features are then applied to a deep framework of convolutional blocks, which retrieves high‐level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification layer. This fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature map. The Berlin Database of Emotional Speech (EMO‐BD) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) datasets are used in experiments to recognise distinct speech emotions. With efficient spectral and temporal feature representation, the proposed SER model achieves 94.2% accuracy for different emotions on the EMO‐BD and 81.1% accuracy on the IEMOCAP dataset respectively. The proposed SER system, DeepCNN, outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO‐BD and IEMOCAP datasets.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2005A database of German emotional speech 2015The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing 2018Speech emotion recognition using deep 1D & 2D CNN LSTM networks 2016Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network 2014Speech emotion recognition using deep neural network and extreme learning machine 2017Automatic speech emotion recognition using recurrent neural networks with local attention 2013The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism

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