Smart emotion recognition framework: A secured IOVT perspective

The promise of automated-driving cars cause the automotive and consumer electronics (CE) sector to rethink what it means to drive, but the relationship between the car and the consumer. Recent trend in Internet of Vehicle Things (IoVT) promotes robust interactions in between humans and vehicles which altimetry points to enhance human abilities such as hearing or emotion awareness as a part of safety concern. The voice-based interactions (speech recognition, stress monitoring) will improve in-time awareness of the vehicle status. Unfortunately, the existing modulation domain speech enhancement techniques achieve low satisfactory performance in detecting humans stress emotions where the environmental noise is inevitable and varies with every passing location of vehicle. In this direction, we propose frontend processing framework, in particular to stress emotion detection cases in different non-stationary noisy environments. This study encompasses three Inter-related issues: (i) analysis, modification, and synthesis of noisy speech emotion in modulation domain in realtime background noise, (ii) extracting set of Mel-frequency cepstral coefficients (MFCC) features from noisy speech stimuli for speech emotion recognition, and (iii) evaluation of overall system performance by means of objective parameters, and confusion matrix in adverse environments using speech emotion database Interactive Emotional Dyadic Motion Capture (IEMOCAP)