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Towards an efficient backbone for preserving features in speech emotion recognition: deep-shallow convolution with recurrent neural network

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Abstract

Speech emotion recognition (SER) has attracted a great deal of research interest, which plays as a critical role in human-machine interactions. Unlike other visual tasks, SER becomes intractable when the convolutional neural networks (CNNs) are employed, owing to their limitation in handling log-mel spectrograms. Therefore, it is useful to establish a feature-extraction backbone that allows CNNs to maintain information integrity of speech utterances when utilizing log-mel spectrograms. Moreover, a neural network with a deep stack of layers can lead to a performance degradation due to various challenges, including information loss, overfitting, or vanishing gradient issues. Many studies employ hybrid/multi-modal methods or specialized network designs to mitigate these obstacles. However, those methods often are unstable, hard to configure and non-adaptive to different tasks. In this research, we propose a reusable backbone pertaining to CNN blocks for undertaking SER tasks, as inspired by the FishNet model. denoted as deep-swallow convolution with RNN (DSCRNN), this proposed backbone method preserves features from both deep and shallow layers, which is effective in improving quality of features extracted from log-mel spectrograms. Simulation results indicate that our proposed DSCRNN backbone achieves improved accuracy rates of 2% and 11% when comparing with those from a baseline model with traditional CNN blocks in a speaker-independent evaluation utilizing the RAVDESS dataset with 4 classes and 8 classes, respectively.

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The code associated with the paper is available at https://github.com/devpriyagoel/deep-shallow-convolution-with-recurrent-neural-network.

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Author notes

  1. Dev Priya Goel and Kushagra Mahajan have contributed equally to this work.

Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology, Guwahati, 781039, India

    Dev Priya Goel, Kushagra Mahajan & Natesan Srinivasan

  2. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, 3216, Australia

    Ngoc Duy Nguyen & Chee Peng Lim

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  1. Dev Priya Goel
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  2. Kushagra Mahajan
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  3. Ngoc Duy Nguyen
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  4. Natesan Srinivasan
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  5. Chee Peng Lim
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Contributions

DPG and KM implement the algorithm and write the paper. NDN, NS, and CPL provides guidance and revise the paper.

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Correspondence to Natesan Srinivasan.

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Goel, D.P., Mahajan, K., Nguyen, N.D. et al. Towards an efficient backbone for preserving features in speech emotion recognition: deep-shallow convolution with recurrent neural network. Neural Comput & Applic 35, 2457–2469 (2023). https://doi.org/10.1007/s00521-022-07723-2

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