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 EmoAudioNetwhich 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 databases. The competitive results call for applying EmoAudioNet on others affects and emotions recognition from speech applications .
Automatic emotions recognition and Major Depressive Disorder (MDD) diagnosis are inherently challenging problems in health informatics applications. According to the World Health Organization, 300M people are affected by depression in 2017 with only the third of them correctly identified. MDD is a permanent mood disorder where the patient constantly feels negative emotions (low Valence) and lacks excitement and interest (low Arousal). Therefore, developing emotionally intelligent systems enhances depression assessment in the early stages and improves the quality of service in depressed patients' care . In this paper, we present a framework for emotions recognition and depression diagnosis from speech called EmoAudioNet. To preserve the high privacy of patient data, only speech is used. The deep EmoAudioNet studies the time-frequency representation of the audio signal and the visual representation of its spectrum of frequencies. In the experimental results, two datasets are used: RECOLA for continuous dimensional emotion recognition and DAIC-WOZ for automatic depression diagnosis . The extensive experiments showed that the proposed approach significantly outperforms previous works in the literature in both datasets .
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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
emotions recognition and Major Depressive Disorder (MDD) diagnosis are inherently challenging problems in health informatics applications. According to the World Health Organization, 300M people are affected by depression in 2017 with only the third of them correctly identified. MDD is a permanent mood disorder where the patient constantly feels negative emotions (low Valence) and lacks excitement and interest (low Arousal). Therefore, developing emotionally intelligent systems enhances depression assessment in the early stages and improves the quality of service in depressed patients' care
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deep Neural Network-based architecture called EmoAudioNetwhich
framework for emotions recognition and depression diagnosis from speech called EmoAudioNet. To preserve the high privacy of patient data, only speech is used. The deep EmoAudioNet
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Two applications are performed using EmoAudioNet : automatic clinical depression recognition and
In the experimental results, two datasets are used: RECOLA for
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from speech
and DAIC-WOZ for automatic depression diagnosis
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the state-of-art approaches on RECOLA and DAIC-WOZ databases. The competitive results call for applying EmoAudioNet on others affects and emotions recognition from speech applications
previous works in the literature in both datasets