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 stagesand improves the quality of service in depressed patients' care . In this paper, we present a framework for emotions recognition and depression diagnosis from speechcalled 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 .
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 .
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Automatic emotions recognition and
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
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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
. MDD describes the constant expression of certain emotions:
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lacks excitement and
lack of
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Therefore, developing emotionally intelligent systems enhances depression assessment in the early stagesand improves the quality of service in depressed patients' care
High-performing intelligent systems would enhance MDD diagnosis in its early stages
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framework for emotions recognition and depression diagnosis from speechcalled EmoAudioNet. To preserve the high privacy of patient data, only speech is used. The deep EmoAudioNet studies
new deep neural network architecture, called EmoAudioNet, for emotion and depression recognition from speech. Deep EmoAudioNet learns from
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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
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