Recognizing low/high anger in speech for call centers
作者： Fu-Ming Lee , Li-Hua Li , Ru-Yi Huang
DOI:
关键词: Decision tree 、 Expression (mathematics) 、 Amateur 、 Feature (machine learning) 、 Speech recognition 、 Quality (business) 、 Variation (game tree) 、 Anger 、 Computer science
摘要: Automatic multi-level anger recognition in speech is an important factor to enhance user satisfaction for call centers. In this research, three emotional states, namely, neutral, low anger, and high of acted corpora with telephone quality are specified emotion recognition. The collected from amateur actors and, thereafter, verified by the themselves. recognizer implemented using Back-propagation Network (BPN) acoustic features examples. Due variation expression methods different person, feature values training examples used too complex make BPN model convergent. To overcome problem, a codified method developed simplify values. With inputs, comparative Decision Tree C5.0 give quite satisfactory test performances Therefore, they can be as part decision support system proper applications
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