Deep ganitrus algorithm for speech emotion recognition

Human emotion recognition with the evaluation of speech signals is an emerging topic in recent decades. Emotion recognition through speech signals is relatively confusing because of the speaking style, voice quality, cultural background of the speaker, environment, etc. Even though numerous signal processing methods and frameworks exists to detect and characterize the speech signal’s emotions, they do not attain the full speech emotion recognition (SER) accuracy and success rate. This paper proposes a novel algorithm, namely the deep ganitrus algorithm (DGA), to perceive the various categories of emotions from the input speech signal for better accuracy. DGA combines independent component analysis with fisher criterion for feature extraction and deep belief network with wake sleep for emotion classification. This algorithm is inspired by the elaeocarpus ganitrus (rudraksha seed), which has 1 to 21 lines. The single line bead is rarest to find, analogously finding a single emotion from the speech signal is also complex. The proposed DGA is experimentally verified on the Berlin database. Finally, the evaluation results were compared with the existing framework, and the test result accomplishes better recognition accuracy when compared with all other current algorithms.

• Publication year

  2022

• Venue

  Journal of Intelligent & Fuzzy Systems

• Publication date

  2022-05-31

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3233/JIFS-201491
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Automatic Speech Emotion Recognition Using Machine Learning

  2021cited by this paper
• Speech emotion recognition using recurrent neural networks with directional self-attention

  2021cited by this paper
• End-to-End Speech Emotion Recognition With Gender Information

  2020cited by this paper
• An Effective Training Scheme for Deep Neural Network in Edge Computing Enabled Internet of Medical Things (IoMT) Systems

  2020cited by this paper
• "I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition

  2020cited by this paper
• Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction

  2020cited by this paper
• ConcealNet: An End-to-end Neural Network for Packet Loss Concealment in Deep Speech Emotion Recognition

  2020cited by this paper
• A novel stochastic deep conviction network for emotion recognition in speech signal

  2020cited by this paper
• Continuous Speech Emotion Recognition with Convolutional Neural Networks

  2020cited by this paper
• Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM

  2020cited by this paper
• A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition

  2019cited by this paper
• An Ensemble Model for Multi-Level Speech Emotion Recognition

  2019cited by this paper
• INCREASING THE PERFORMANCE OF SPEECH RECOGNITION SYSTEM BY USING DIFFERENT OPTIMIZATION TECHNIQUES TO REDESIGN ARTIFICIAL NEURAL NETWORK

  2019cited by this paper
• Decision tree SVM model with Fisher feature selection for speech emotion recognition

  2019cited by this paper
• Semi-Supervised Speech Emotion Recognition With Ladder Networks

  2019cited by this paper
• A novel system for effective speech recognition based on artificial neural network and opposition artificial bee colony algorithm

  2019cited by this paper
• Accurate Speech Emotion Recognition by using Brain-Inspired Decision-Making Spiking Neural Network

  2019cited by this paper
• Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching

  2018cited by this paper
• A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal

  2017cited by this paper
• FDBN: Design and development of Fractional Deep Belief Networks for speaker emotion recognition

  2016influential reference
• Emotional Prosody Processing for Nonnative English Speakers

  2016cited by this paper
• Speech Emotion Recognition Using Fourier Parameters

  2015cited by this paper
• A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

  2014cited by this paper
• Contrastive divergence learning for the Restricted Boltzmann Machine

  2013cited by this paper
• Analysis and Design of Digital IIR Integrators and Differentiators Using Minimax and Pole, Zero, and Constant Optimization Methods

  2013cited by this paper
• Survey on speech emotion recognition: Features, classification schemes, and databases

  2011cited by this paper
• Novel class of stable wideband recursive digital integrators and differentiators

  2010cited by this paper
• Unsupervised Pattern Discovery in Speech

  2008cited by this paper
• Feature Selection by Combining Fisher Criterion and Principal Feature Analysis

  2007cited by this paper
• On Contrastive Divergence Learning

  2005cited by this paper
• Toward an Integrative Multimodeling Interface: A Human-Computer Interface Approach to Interrelating Model Structures

  2004cited by this paper
• Predictors of Burden for Caregivers of Patients with Parkinson's Disease

  2002cited by this paper
• Independent component analysis: an introduction.

  2002cited by this paper
• The "wake-sleep" algorithm for unsupervised neural networks.

  1995cited by this paper
• Turn Signals Are The Facial Expressions Of Automobiles

  1992cited by this paper

• Cover Song Recognition Using a Modified Similarity Network Fusion Algorithm

  2025cites this paper
• Speech Emotion Recognition Technology in Human-Computer Interaction

  2025cites this paper
• A novel emotion recognition method based on 1D-DenseNet

  2023cites this paper
• Classification Arabic and Dialect Iraqi Spoken to Sign- Language Based on Iraqi Dictionary

  2022cites this paper