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4. Multi-Conditioning And Data Augmentation Using Generative Noise Model For Speech Emotion Recognition In Noisy Conditions

Multi-Conditioning And Data Augmentation Using Generative Noise Model For Speech Emotion Recognition In Noisy Conditions

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IEEE ICASSP 2020 Virtual Conference May 2020

Degradation due to additive noise is a significant road block in the real-life deployment of Speech Emotion Recognition (SER) systems. Most of the previous work in this field dealt with the noise degradation either at the signal or at the feature level. I

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Multi-Conditioning And Data Augmentation Using Generative Noise Model For Speech Emotion Recognition In Noisy Conditions

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Degradation due to additive noise is a significant road block in the real-life deployment of Speech Emotion Recognition (SER) systems. Most of the previous work in this field dealt with the noise degradation either at the signal or at the feature level. I