Intelligent monitoring systems and affective computing applications have emerged in recent years ... more Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include assessment of affective states such as Major Depressive Disorder (MDD). MDD describes the constant expression of certain emotions: negative emotions (low Valence) and lack of interest (low Arousal). High-performing intelligent systems would enhance MDD diagnosis in its early stages. In this paper, we present a new deep neural network architecture, called EmoAudioNet, for emotion and depression recognition from speech. Deep EmoAudioNet learns from the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies. Our model outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30%, 91.44% and 73.25% in predicting arousal, valence, and depression, respectively.
Automatic analysis of emotions and affects from speech is an inherently challenging problem with ... more Automatic analysis of emotions and affects from speech is an inherently challenging problem with a broad range of applications in Human-Computer Interaction (HCI), health informatics, assistive technologies and multimedia retrieval. Understanding human's specific and basic emotions and reacting accordingly can improve HCI. Besides, giving machines skills to understand human's emotions when interacting with other humans can help humans with a socio-affective intelligence. In this paper, we present a deep Neural Network-based architecture called EmoAudioNet which studies the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies. Two applications are performed using EmoAudioNet : automatic clinical depression recognition and continuous dimensional emotion recognition from speech. The extensive experiments showed that the proposed approach significantly outperforms the state-of-art approaches on RECOLA and DAIC-WOZ databas...
Intelligent monitoring systems and affective computing applications have emerged in recent years ... more Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include assessment of affective states such as Major Depressive Disorder (MDD). MDD describes the constant expression of certain emotions: negative emotions (low Valence) and lack of interest (low Arousal). High-performing intelligent systems would enhance MDD diagnosis in its early stages. In this paper, we present a new deep neural network architecture, called EmoAudioNet, for emotion and depression recognition from speech. Deep EmoAudioNet learns from the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies. Our model outperforms the state-of-the-art methods for RECOLA and for DAIC-WOZ datasets and it reaches high accuracies of 89.30%, 91.44% and 73.25% in predicting arousal, valence, and depression, respectively.
Automatic analysis of emotions and affects from speech is an inherently challenging problem with ... more Automatic analysis of emotions and affects from speech is an inherently challenging problem with a broad range of applications in Human-Computer Interaction (HCI), health informatics, assistive technologies and multimedia retrieval. Understanding human's specific and basic emotions and reacting accordingly can improve HCI. Besides, giving machines skills to understand human's emotions when interacting with other humans can help humans with a socio-affective intelligence. In this paper, we present a deep Neural Network-based architecture called EmoAudioNet which studies the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies. Two applications are performed using EmoAudioNet : automatic clinical depression recognition and continuous dimensional emotion recognition from speech. The extensive experiments showed that the proposed approach significantly outperforms the state-of-art approaches on RECOLA and DAIC-WOZ databas...
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Papers by Romain Alfred