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Towards an efficient backbone for preserving features in speech emotion recognition: deep-shallow convolution with recurrent neural network
journal contribution
posted on 2022-11-22, 02:49 authored by DP Goel, K Mahajan, ND Nguyen, N Srinivasan, Chee Peng LimChee Peng LimSpeech 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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Journal
Neural Computing and ApplicationsLocation
Berlin, GermanyPublisher DOI
ISSN
0941-0643eISSN
1433-3058Language
EnglishPublication classification
C1 Refereed article in a scholarly journalPublisher
SpringerUsage metrics
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Keywords
CNNComputer ScienceComputer Science, Artificial IntelligenceDeep learningFishNetHuman machine interactionIEMOCAPLog-mel spectrogramMODELRAVDESSRNNScience & TechnologySERSpeech emotion recognitionTechnologyClinical ResearchCognitive SciencesArtificial Intelligence and Image ProcessingElectrical and Electronic Engineering not elsewhere classified
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