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Speech Emotion Recognition Using Mel Frequency Log Spectrogram and Deep Convolutional Neural Network

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Futuristic Communication and Network Technologies (VICFCNT 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 792))

Abstract

In recent years, speech emotion recognition (SER) has engrossed more attention in speech processing because of its potential in various speech-based intelligent systems. In deep learning algorithms to capture discriminative features of the audio emotion samples, a large number of features are required, which increases the computational complexity of the network. This paper presents a three-layered sequential deep convolutional neural network (DCNN) based on mel frequency log spectrogram (MFLS) for emotion recognition. Mel frequency log spectrogram that confines the salient information from the emotion speech corpus and two-dimensional DCNN. Exploratory outcomes on the Berlin Emo-DB dataset show that the proposed method gives 95.68 and 96.07% accuracy for the speaker-dependent and speaker-independent approaches. The performance of the proposed method is compared with CNN and CNN-LSTM on the Berlin Emo-DB dataset and results in improved accuracy.

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Authors and Affiliations

  1. SENSE, VIT, Chennai, India

    Kishor Bhangale & K. Mohanaprasad

Authors
  1. Kishor Bhangale
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  2. K. Mohanaprasad
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Corresponding author

Correspondence to K. Mohanaprasad .

Editor information

Editors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    A. Sivasubramanian

  2. Department of Electrical and Computer Engineering, Bradley University, Peoria, IL, USA

    Prasad N. Shastry

  3. Department of Electrical and Electronic Engineering, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Malaysia

    Pua Chang Hong

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Bhangale, K., Mohanaprasad, K. (2022). Speech Emotion Recognition Using Mel Frequency Log Spectrogram and Deep Convolutional Neural Network. In: Sivasubramanian, A., Shastry, P.N., Hong, P.C. (eds) Futuristic Communication and Network Technologies. VICFCNT 2020. Lecture Notes in Electrical Engineering, vol 792. Springer, Singapore. https://doi.org/10.1007/978-981-16-4625-6_24

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  • DOI: https://doi.org/10.1007/978-981-16-4625-6_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-4624-9

  • Online ISBN: 978-981-16-4625-6

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