Knowledge enhancement for speech emotion recognition via multi-level acoustic feature

Zhao, Huan; Huang, Nianxin; Chen, Haijiao

doi:10.1080/09540091.2024.2312103

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…The complete review of various SER and performance over the different data set is presented [40]. The performance of the SER using different ML is tabulated [41].…”

Section: For Sermentioning

confidence: 99%

A New Approach for Speech Emotion Recognition Using Single Layered Convolutional Neural Network

J, V Vinoth Kumar,

(Corresponding Author),

Khan

et al. 2024

MJCS

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Creating a computational device to identify human emotions via voice analysis represents a notable achievement in the sector of human-computer interaction, especially within the healthcare domain. We propose a new light-weight model for addressing challenges of emotions recognition. The model works based on CNN with change of kernel processing. The proposed model performs a direct matching to recognize speech emotions of different eight categories using a statistical model named Analysis of Variance (ANOVA) as kernel for features extraction and Cosine Similarity Measurement (CSM) as activation function for CNN model. This proposed model contains eight-folded single-layered intermediate neurons, and each neuron can segregate speech emotion pattern using CSM from the voice convergence matrix to explore a part of the solution from the whole solution. Experiment results demonstrates that the proposed model outperforms compared with multiple layered existing CNN methods in identifying the emotional state of a speaker.

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“…The complete review of various SER and performance over the different data set is presented [40]. The performance of the SER using different ML is tabulated [41].…”

Section: For Sermentioning

confidence: 99%

A New Approach for Speech Emotion Recognition Using Single Layered Convolutional Neural Network

J, V Vinoth Kumar,

(Corresponding Author),

Khan

et al. 2024

MJCS

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PCQ: Emotion Recognition in Speech via Progressive Channel Querying

Wang,

et al. 2024

Lecture Notes in Computer Science

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Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases

Sandulescu,

Ianculescu,

Valeanu

et al. 2024

Algorithms

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Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: a) there is a strong correlation between physical and mental health, and b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases.

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Knowledge enhancement for speech emotion recognition via multi-level acoustic feature

Cited by 3 publications

References 27 publications

A New Approach for Speech Emotion Recognition Using Single Layered Convolutional Neural Network

A New Approach for Speech Emotion Recognition Using Single Layered Convolutional Neural Network

PCQ: Emotion Recognition in Speech via Progressive Channel Querying

Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases

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