Speech emotion recognition based on multi-feature speed rate and LSTM

Yang, Zijun; Li, Zhen; Zhou, Shi; Zhang, Lifeng; Serikawa, Seiichi

doi:10.1016/j.neucom.2024.128177

Neurocomputing

2024

DOI: 10.1016/j.neucom.2024.128177

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Speech emotion recognition based on multi-feature speed rate and LSTM

Zijun Yang,

Zhen Li,

Shi Zhou

et al.

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2024

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Cited by 3 publications

References 49 publications

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Speech emotion recognition for human–computer interaction

Thiripurasundari,

Bhangale,

Aashritha

et al. 2024

Int J Speech Technol

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Speech emotion recognition for human–computer interaction

Thiripurasundari,

Bhangale,

Aashritha

et al. 2024

Int J Speech Technol

View full text Add to dashboard Cite

E‐Speech: Development of a Dataset for Speech Emotion Recognition and Analysis

Liu,

Shi,

Zhang

et al. 2024

International Journal of Intelligent Systems

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Speech emotion recognition plays a crucial role in analyzing psychological disorders, behavioral decision‐making, and human‐machine interaction applications. However, the majority of current methods for speech emotion recognition heavily rely on data‐driven approaches, and the scarcity of emotion speech datasets limits the progress in research and development of emotion analysis and recognition. To address this issue, this study introduces a new English speech dataset specifically designed for emotion analysis and recognition. This dataset consists of 5503 voices from over 60 English speakers in different emotional states. Furthermore, to enhance emotion analysis and recognition, fast Fourier transform (FFT), short‐time Fourier transform (STFT), mel‐frequency cepstral coefficients (MFCCs), and continuous wavelet transform (CWT) are employed for feature extraction from the speech data. Utilizing these algorithms, the spectrum images of the speeches are obtained, forming four datasets consisting of different speech feature images. Furthermore, to evaluate the dataset, 16 classification models and 19 detection algorithms are selected. The experimental results demonstrate that the majority of classification and detection models achieve exceptionally high recognition accuracy on this dataset, confirming its effectiveness and utility. The dataset proves to be valuable in advancing research and development in the field of emotion recognition.

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Multimodal driver emotion recognition using motor activity and facial expressions

Espino-Salinas,

Luna-García,

Celaya-Padilla

et al. 2024

Front. Artif. Intell.

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Driving performance can be significantly impacted when a person experiences intense emotions behind the wheel. Research shows that emotions such as anger, sadness, agitation, and joy can increase the risk of traffic accidents. This study introduces a methodology to recognize four specific emotions using an intelligent model that processes and analyzes signals from motor activity and driver behavior, which are generated by interactions with basic driving elements, along with facial geometry images captured during emotion induction. The research applies machine learning to identify the most relevant motor activity signals for emotion recognition. Furthermore, a pre-trained Convolutional Neural Network (CNN) model is employed to extract probability vectors from images corresponding to the four emotions under investigation. These data sources are integrated through a unidimensional network for emotion classification. The main proposal of this research was to develop a multimodal intelligent model that combines motor activity signals and facial geometry images to accurately recognize four specific emotions (anger, sadness, agitation, and joy) in drivers, achieving a 96.0% accuracy in a simulated environment. The study confirmed a significant relationship between drivers' motor activity, behavior, facial geometry, and the induced emotions.

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Speech emotion recognition based on multi-feature speed rate and LSTM

Cited by 3 publications

References 49 publications

Speech emotion recognition for human–computer interaction

Speech emotion recognition for human–computer interaction

E‐Speech: Development of a Dataset for Speech Emotion Recognition and Analysis

Multimodal driver emotion recognition using motor activity and facial expressions

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