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This work proposes the arousal-selection and feature-selection schemes to improve speaker's gender and age identification performance. Our previous results showed that gender and age recognition rates would increase as affective stimulation degrees were lower and higher, respectively. Considering a practical scenario, the speaker's mood does not alter frequently, so speech frames are partitioned into...
This work proposes an online interactive-duet KTV system with emotional scoring, which consists of real-time communication, synchronous playback, chatting box and emotional scoring mechanism. The experimental results demonstrate that most of time differences between two users are less than 50ms and emotional scoring is very promising.
In this work, a typical hierarchical Support Vector Machine (SVM) classifier structure with three stages is adopted to identify 6 affective modes of happy, angry, excited, nervous, sad and calm from Musical TeleVision (MTV) sequences, which comprise audio and video signals. To comprehend emotional modes, audio features including the spectral centroid, spectral spread, zero crossing rate, peak of zero...
In this work, emotion-inspired age and gender recognition systems are developed. In the beginning, speakers' utterances with emotions of angry, happy, calm and sad are analyzed to identify their ages and genders where the recognition engine adopts a Support Vector Machine (SVM). According to the experimental results, the accuracies of the age and gender recognitions under a low arousal emotion tend...
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