Speech Emotion Recognition for Call Analytics
Mel-frequency cepstral coefficients (MFCC) feature extractor and 2D-CNN classifying call center customer audio emotion states.
Project Overview
Processes customer support call recordings, extracts acoustic pitch and energy contours, and classifies caller sentiment (angry, calm, frustrated, happy) to escalate dissatisfied customers to senior supervisors. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Speech Emotion Recognition for Call Analytics.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.