Application of PNN-HMM Model Based on Emotion-Speech Combination in Broadcast Intelligent Communication Analysis

Yang, Hua

doi:10.1109/access.2023.3301127

IEEE Access

2023

DOI: 10.1109/access.2023.3301127

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Application of PNN-HMM Model Based on Emotion-Speech Combination in Broadcast Intelligent Communication Analysis

Hua Yang¹

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2024

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Cited by 2 publications

References 23 publications

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An Innovative Method for Speech Signal Emotion Recognition Based on Spectral Features Using GMM and HMM Techniques

Al-Dujaili Al-Khazraji,

Ebrahimi-Moghadam

2024

Wireless Pers Commun

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An Innovative Method for Speech Signal Emotion Recognition Based on Spectral Features Using GMM and HMM Techniques

Al-Dujaili Al-Khazraji,

Ebrahimi-Moghadam

2024

Wireless Pers Commun

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A Text-based Intelligently driven Emotion Recognition Framework

Wu,

2024

Salud, Ciencia y Tecnología

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Introduction: Emotion recognition from text has gained considerable interest due to its applications in human-computer interaction, emotion analysis, and psychiatric research. Traditional methods have struggled with emotional ambiguity, cultural nuances, and the dynamic nature of language, which affect the reliability of emotion recognition. This paper presents a novel emotion recognition framework named Artificial Gorilla Troops driven Modified Gaussian Support Vector Machine (AGT-MGSVM).Methods: We gathered a publicly available ISEAR dataset containing various textual emotional expressions and applied natural language processing (NLP) techniques for text pre-processing. The suggested AGT-MGSVM approach combines the resilience of the Gaussian assist vector gadget (GSVM) with the ability of AGT, a bio-stimulated optimization method. AGT improves the MGSVM with the aid of dynamically regulating its parameters based on the evolutionary conduct of gorilla troops, optimizing the version to enhance emotion popularity. Results: We examine the performance of the proposed technique against traditional emotion reputation methods using standard metrics inclusive of recall (89.2%), precision (89.5%), F1-score (89.4%), and accuracy (89.9%). Conclusion: The counseled AGT-MGSVM method is a promising improvement in intelligence-driven emotion reputation from the text.

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Application of PNN-HMM Model Based on Emotion-Speech Combination in Broadcast Intelligent Communication Analysis

Cited by 2 publications

References 23 publications

An Innovative Method for Speech Signal Emotion Recognition Based on Spectral Features Using GMM and HMM Techniques

An Innovative Method for Speech Signal Emotion Recognition Based on Spectral Features Using GMM and HMM Techniques

A Text-based Intelligently driven Emotion Recognition Framework

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