ESERNet: Learning spectrogram structure relationship for effective speech emotion recognition with swin transformer in classroom discourse analysis

Liu, Tingting; Wang, Minghong; Yang, Bing; Liu, Hai; Yi, Shaoxin

doi:10.1016/j.neucom.2024.128711

Neurocomputing

2025

DOI: 10.1016/j.neucom.2024.128711

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ESERNet: Learning spectrogram structure relationship for effective speech emotion recognition with swin transformer in classroom discourse analysis

Tingting Liu,

Minghong Wang,

Bing Yang

et al.

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A Helium Speech Correction Method Based on Generative Adversarial Networks

Li,

Chen,

et al. 2024

BDCC

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The distortion of helium speech caused by helium−oxygen gas mixtures significantly impacts the safety and communication efficiency of saturation divers. Although existing correction methods have shown some effectiveness in improving the intelligibility of helium speech, challenges remain in enhancing clarity and high−pitch correction. To address the issue of degraded speech quality post−correction, a novel helium speech correction method based on generative adversarial networks (GANs) is proposed. Firstly, a new helium speech dataset is introduced, which includes isolated words and continuous speech in both Chinese and English. By training and testing on both isolated words and continuous passages, the correction capability of the model can be accurately evaluated. Secondly, a new evaluation system for helium speech correction is proposed, which partially fills the gap in current helium speech evaluation metrics. This system uses comprehensive similarity to evaluate the similarity of keywords at the sentence level, thus assessing the correction results of helium speech from both word and sentence dimensions. Lastly, a GAN−based helium speech correction method is designed. This method solves the problems of pitch period distortion and formant shift in helium speech by introducing an adaptive speech segmentation algorithm and a fusion loss function and significantly improves the clarity and intelligibility of corrected helium speech. The experimental results show that the corrected helium speech is improved in clarity and intelligibility, which shows its practical value and application potential.

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A Helium Speech Correction Method Based on Generative Adversarial Networks

Li,

Chen,

et al. 2024

BDCC

View full text Add to dashboard Cite

show abstract

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ESERNet: Learning spectrogram structure relationship for effective speech emotion recognition with swin transformer in classroom discourse analysis

Cited by 1 publication

References 42 publications

A Helium Speech Correction Method Based on Generative Adversarial Networks

A Helium Speech Correction Method Based on Generative Adversarial Networks

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