Voice-activity home care system

This work proposes a voice-activity home care system which can construct a life log associated with voices at home. Accordingly, the techniques of sound-pressure-level calculation, abnormal sound detection, noise reduction, text-independent speaker recognition and keyword spotting are developed. In abnormal sound detection and speaker recognition, we adopt the two-stage recognition processes of Gaussian Mixture Model (GMM) for sound rejection, and Support Vector Machine (SVM) for sound classification. The experimental results reveal that the proposed abnormal sound detection, speaker recognition, and word spotting can reach accuracy rates above 82%, 90%, and 87%, respectively. Based on the recognized abnormal sounds or special words, an emergent event can be identified for home care where a speaker is known as well. Finally, the abovementioned recognition results versus time scales can fairly build a daily life log for home care.