Speech Emotion Recognition with Application to Mental Health: A Tensor Perspective

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dc.contributor.authorPandey, Sandeep Kumar
dc.date.accessioned2023-01-25T08:03:15Z
dc.date.accessioned2023-10-20T07:27:03Z
dc.date.available2023-01-25T08:03:15Z
dc.date.available2023-10-20T07:27:03Z
dc.date.issued2022
dc.descriptionSupervisors: Shekhawat, Hanumant Singh and Prasanna, S R Mahadevaen_US
dc.description.abstractSpeech Emotion Recognition (SER) has been an active area of research ever since the need for smooth and natural Human-Computer Interaction (HCI) came into play. This thesis aims to develop an SER system based on an amalgamation of Tensor Factorization and Neural Network-based learning to mitigate several issues while using contemporary deep learning architectures. This, in turn, is helpful towards recognizing the mental health issues such as depression, anxiety, etc., from speech signals as it is shown in the literature that mental health and emotions are highly correlated. As such, this thesis tries to provide techniques to incorporate emotional information to assess mental health conditions from speech signals, thereby helping the psychologists assign a depression score to patients based on their experience and machine-generated score, thereby mitigating any human bias which might creep in human-only situations.en_US
dc.identifier.otherROLL NO.156302006
dc.identifier.urihttps://gyan.iitg.ac.in/handle/123456789/2269
dc.language.isoenen_US
dc.relation.ispartofseriesTH-2937;
dc.subjectSpeech Emotion Recognitionen_US
dc.subjectDeep Learningen_US
dc.subjectTensor Factorizationen_US
dc.subjectMental Healthen_US
dc.subjectDepression Diagnosisen_US
dc.subjectMulti-culturalen_US
dc.subjectFusionen_US
dc.subjectMulti- modalen_US
dc.subjectMulti-tasken_US
dc.titleSpeech Emotion Recognition with Application to Mental Health: A Tensor Perspectiveen_US
dc.typeThesisen_US
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PhD Theses (Electronics and Electrical Engineering)