By Glise, anthony
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Extra resources for A study in phrasing
Biosensors possess a number of advantages against other emotion recognition methods as they can be made both inobtrusive and robust against a number of environmental conditions which other forms of emotion recognition have difficulty to overcome. In this paper, we describe a procedure to train computers to recognise emotions using multiple signals from many different bio-sensors. In particular, we describe the procedure we adopted to elicit emotions and to train our system to recognise them. 9% for recognition of emotion arousal and valence respectively.
However, the measured loudness depends also on the distance of the speaker to the microphone. Possible workarounds are methods to localize the speaker by using several microphones and normalize by the distance or the use of close talk microphones. But these are restricting the range of possible applications more than necessary. In contrast, energy normalization allows the use of emotion detection systems in different environments. Noise is also influencing the performance of the classifier. 2, techniques from speech recognition like training with contaminated data, can compensate to some extent for the influence of additive noise.
4. Box plots of normalized amplitude quotient values with regard to different genders and emotions. standard deviation values for joy, sadness and tenderness than for neutral indicate larger variation in the voice quality within these emotions. The relative order of the NAQ value means between different emotions was the same for both males and females. The order from smallest to largest was 20 M. Airas and P. Alku angry, neutral, joy, sadness and tenderness. Importantly, when comparing males versus females, it can readily be noted that the variation between different emotions was larger in females than in males.