Development of AI-Powered Speech Emotion Recognition System for Workplace Psychological Monitoring

In modern organizations, effective team collaboration is often hindered by misunderstandings, hidden conflicts, and unrecognized psychological issues. Managers and team members lack reliable tools to assess emotional well-being during voice interactions, which can lead to decreased productivity, increased conflict, and overlooked mental health concerns, especially under high workload and remote working conditions.

A mid-to-large size enterprise specializing in professional consulting services, seeking to enhance employee well-being and communication efficiency through innovative AI solutions.

• Develop an AI-powered system capable of automatically detecting emotional states from speech during voice communications to assist managers in understanding team members' psychological well-being.
• Create a software solution that analyzes audio recordings to identify dominant emotions such as anger, disgust, fear, happiness, neutrality, and sadness, with probability levels.
• Enable proactive management interventions to reduce misunderstandings, conflicts, and mental health risks, thereby improving overall communication quality and team cohesion.
• Support real-time or post-interaction emotion analysis using audio data captured from various sources including calls, video recordings, and voice messages.
• Reduce managerial workload by automating the emotional analysis process and providing actionable insights for team health management.

• Audio Input Processing: Accepts audio files extracted from voice or video recordings, supporting various formats and low to medium quality recordings.
• Speech Segmentation & Spectrogram Creation: Identifies speech segments within audio, builds spectrograms representing signal frequency over time.
• Feature Extraction: Extracts features including PRAAT parameters (fundamental frequency, pitch, harmonic to noise ratio, jitter, shimmer, intensity, formants), MFCC characteristics, nonlinear voice features, and pause metrics.
• Emotion Detection Model: Utilizes a pretrained deep learning model to analyze features and classify six emotions with associated probability scores.
• Result Visualization: Provides a clear representation of dominant emotions and confidence levels, highlighting emotional mismatch indicators in cases of psychological distress.
• Compatibility & Integration: Supports integration with internal communication systems and dashboards for seamless deployment.

• Integration with internal voice communication platforms (e.g., VoIP, conference call systems)
• Connection to organizational dashboards and internal analytics tools
• APIs for uploading and processing audio files from various sources, including recorded calls, videos, and voice messages

• High accuracy in emotion classification with minimal false positives/negatives
• Real-time or near-real-time processing latency suitable for workplace applications
• Robustness to audio variability and background noise typical in real-life recordings
• Scalability to handle large volumes of audio data across multiple teams and departments
• Data privacy and security compliance to protect sensitive employee information

Implementing this AI speech emotion recognition system aims to improve workplace communication and psychological well-being by enabling early detection of emotional distress. Expected benefits include a reduction in misunderstandings and conflicts, enhanced team cohesion, and increased overall productivity. The system's deployment could lead to better mental health monitoring and proactive management, contributing to a healthier and more engaged organizational environment.