A novel dilated Bi-LSTM framework for depression detection from speech signals through feature fusion

Key Points

• Depression detection achieved accuracy rates up to 90% with the Bi-LSTM model.
• Feature fusion helps improve the model's performance over traditional methods.
• Utilization of deep learning techniques on speech signals enables real-time analysis.
• Highlights the potential for integrating AI in mental health assessments, emphasizing the need for further validation.