A feature selection model for speech emotion recognition using clustering-based population generation with hybrid of equilibrium optimizer and atom search optimization algorithm

Chattopadhyay, Soham; Dey, Arijit; Singh, Pawan Kumar; Ahmadian, Ali; Sarkar, Ram

doi:10.1007/s11042-021-11839-3

Cited by 19 publications

(3 citation statements)

References 72 publications

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“…In 2022 [230], Chattopadhyay et al introduced the Clustering-based Equilibrium Optimizer and Atom Search Optimization algorithm (CEOAS), a hybridized EO that uses both EO and ASO to help reduce feature dimensions and hence increase classification accuracy. Additionally, it is clear from the experimental results that the proposed CEOAS outperforms other state-of-the-art metaheuristics for speech emotion identification.…”

Section: Hybridization With Atom Search Optimization (Aso)mentioning

confidence: 99%

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rai

Dhal

2023

Arch Computat Methods Eng

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There have been many algorithms created and introduced in the literature inspired by various events observable in nature, such as evolutionary phenomena, the actions of social creatures or agents, broad principles based on physical processes, the nature of chemical reactions, human behavior, superiority, and intelligence, intelligent behavior of plants, numerical techniques and mathematics programming procedure and its orientation. Nature-inspired metaheuristic algorithms have dominated the scientific literature and have become a widely used computing paradigm over the past two decades. Equilibrium Optimizer, popularly known as EO, is a population-based, nature-inspired meta-heuristics that belongs to the class of Physics based optimization algorithms, enthused by dynamic source and sink models with a physics foundation that are used to make educated guesses about equilibrium states. EO has achieved massive recognition, and there are quite a few changes made to existing EOs. This article gives a thorough review of EO and its variations. We started with 175 research articles published by several major publishers. Additionally, we discuss the strengths and weaknesses of the algorithms to help researchers find the variant that best suits their needs. The core optimization problems from numerous application areas using EO are also covered in the study, including image classification, scheduling problems, and many others. Lastly, this work recommends a few potential areas for EO research in the future.

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Section: Hybridization With Atom Search Optimization (Aso)mentioning

confidence: 99%

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Rai

Dhal

2023

Arch Computat Methods Eng

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“…They introduce sounds of differing intensities, alter the noises over time, and combine different types of noise to see how well our model holds up under stress. The study (Chattopadhyay et al 2023) approached assistance in decreasing the feature dimension but also boosts the learning model's classification precision. In addition to its central position in human communication, speech is also the primary information exchange channel in HCI.…”

Section: Related Workmentioning

confidence: 99%

Spoken emotion recognition through human-computer interaction using a novel deep learning technology

Bargavi S. K.,

Bhambu,

Gupta

2023

Multidiscip. Sci. J.

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The paradigm of textual or display-based control in human-computer interaction (HCI) has changed in favor of more understandable control methods, such as gesture, voice, and imitation. Speech in particular contains a large quantity of information, revealing the speaker's inner state as well as his or her goal and intention. The speaker's request can be understood through language analysis, but additional speech features show the speaker's mood, purpose, and intention. As a consequence, in modern HCI systems, emotion identification from speech has become crucial. Additionally, it is challenging to aggregate the results of the many professionals engaged in emotion identification. There have been several methods for analyzing sound in the past. However, it was impossible to analyses people's emotions during a live speech. Studies on real-time data are now more prominent than ever because of the advancement of artificial intelligence and the great performance of deep learning techniques. This research uses a cutting-edge deep-learning technique to identify emotions in human speech. The research made use of the open-source Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. More than 2000 fragments of data were captured by 24 performers as speeches and songs for the RAVDESS dataset. The actors' responses to eight distinct moods were recorded. It was designed to find various emotion classifications. In this study, a novel neuro-fuzzy swallow swarm-optimized deep convolutional neural networks (NFSO-DCNN) approach for classification was suggested. The performance of the suggested model was compared to that of similar research, and the outcomes were assessed. Employing the suggested example on the RAVDESS dataset, an overall accuracy of 98.5% was attained for categorizing emotions

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“…In the early days of SER, researchers presented machine learning algorithms, fed with low-level descriptors, for the classification and regression of emotional spoken instances [31,32,33,34,35]. More recently, Soham et al [36] utilised Linear Prediction Coding (LPC) and Linear Predictive Cepstral Coefficient (LPCC) features in a hybrid wrapper feature selection algorithm combined with a Support Vector Machine (SVM). The approach by Tao et al [37], based on LLDs trained in a variety of models, including SVM and attention based on pooling RNN, was also verified on multitask SER [38].…”

Section: Related Workmentioning

confidence: 99%

A Residual Multi-Scale Convolutional Transformer Network with Chunk-level Log-Mel Spectrograms for Speech Emotion Recognition

Yan¹,

Wang²,

Parada-Cabaleiro³

et al. 2022

Preprint

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<p>The great variety of human emotional expression as well as the differences in the ways they perceive and annotate them make Speech Emotion Recognition (SER) an ambiguous and challenging task. With the development of deep learning, long-term progress has been made in supervised SER systems. However, the existing convolutional neural networks present certain limitations, such as their inability to well capture global features, which contain important emotional information. In addition, due to the subjective nature and continuity of emotion, the instance segments in which emotional speech is typically segmented do not fully reflect the true labels and cannot describe dynamic temporal changes. Thus, accurate emotional representation cannot be learnt in the process of feature extraction. In order to overtake these limitations, we propose an end-to-end network only for speech that maps sequences of different lengths to a fixed number of chunks and strictly preserves the order of chunks by adaptively adjusting their overlap. Subsequently, it extracts log-mel spectrogram features from chunk-level segments and feeds them into the Residual Multi-Scale Convolutional Neutral Networks with Transformer(RMSCTx) model framework. Finally, by keeping the order of the chunk-level segments, a temporal domain mean layer is used to further extract utterance-level feature representations. With this method, we perform multidimensional SER, i. e., the prediction of arousal, valence, and dominance. The experimental results on three popular corpora demonstrate not only the superiority of our approach, but also the robustness of the model for SER, showing an improvement of the recognition accuracy in the newest version of the public dataset MSP-Podcast (1.9).</p>

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A feature selection model for speech emotion recognition using clustering-based population generation with hybrid of equilibrium optimizer and atom search optimization algorithm

Cited by 19 publications

References 72 publications

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Spoken emotion recognition through human-computer interaction using a novel deep learning technology

A Residual Multi-Scale Convolutional Transformer Network with Chunk-level Log-Mel Spectrograms for Speech Emotion Recognition

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