Detecting Depression through Dialogue: A Comprehensive Review of Speech Recognition Technologies
Keywords:
Depression, Dialogue, Speech recognition, mental health diagnostics, Acoustic features, Prosodic features, Linguistic features, Machine learning, Early diagnosis, Personalized treatment, Ethical considerations, Data privacy, Interpretability, Clinical deployment.Abstract
Detecting depression through dialogue represents a promising approach to mental health assessment, leveraging advancements in speech recognition technologies. This paper provides a comprehensive review of speech recognition technologies utilized for depression detection, examining their methodologies, capabilities, and challenges. Speech, as a fundamental mode of communication, carries valuable information about individuals' emotional and cognitive states. Machine learning algorithms, such as support vector machines and neural networks, are commonly employed to analyze speech patterns and classify depressive symptoms. Challenges including variability in speech data and ethical considerations surrounding data privacy are addressed. Through interdisciplinary collaboration and ongoing research, speech recognition technologies hold the potential to revolutionize depression diagnosis and improve mental health care outcomes. This paper provides a comprehensive review of speech recognition systems tailored for identifying depressive symptoms through dialogue analysis. Speech, as a rich source of information, offers valuable insights into individuals' emotional and cognitive states, making it an attractive target for automated analysis. Key features extracted from speech, including acoustic, prosodic, and linguistic elements, are analyzed using machine learning algorithms to discern patterns indicative of depression. The review discusses methodologies, challenges, and recent advancements in speech recognition for depression detection, emphasizing the potential of these technologies in facilitating early diagnosis and personalized treatment approaches. Furthermore, ethical considerations, data privacy concerns, and the need for interpretability in machine learning models are addressed to ensure responsible and ethical deployment in clinical settings. Overall, this comprehensive review aims to highlight the transformative potential of speech recognition technologies in enhancing mental health care by providing objective, scalable, and personalized approaches to depression diagnosis and intervention.
