Title :
Detecting emotion from voice using selective Bayesian pairwise classifiers
Author :
Cho, Jangsik ; Kato, Shohei
Author_Institution :
Dept. of Comput. Sci. & Eng., Nagoya Inst. of Technol., Nagoya, Japan
Abstract :
This paper describes a method for detecting a dialogist´s emotion from his or her voice. The method is based on pairwise classification by probability from the selective pairwise classifiers. In this research, we focus on the elements of emotion included in a dialogist´s voice. Thus, as training datasets for learning the pairwise classifiers, we extract acoustic features from emotionally expressive voice samples spoken by unspecified actors and actresses in films, and TV dramas. The acoustic features adopt duration per single mora, fundamental frequency, energy, and formant. All features except duration per single mora have statistics extracted standard deviation, mean, maximum, minimum, median, timezone of maximum, and timezone of minimum. Pairwise classification classifies a multi-class problem by using a series of binary classifiers. Pairwise classifiers used tree augmented naive bayes, which constructs tree structure among the attributes in naive bayes, by selected subset features. The subset features are selected on every pair of emotions by using naive bayes. This paper reports the accuracy rates of emotion detection by using our method. In experimental results from our voice samples, the emotion classification rates improved.
Keywords :
Bayes methods; acoustic signal detection; emotion recognition; feature extraction; probability; set theory; signal classification; speech processing; acoustic feature extraction; binary classifiers; dialogist emotion detection; emotionally expressive voice samples; fundamental frequency; multiclass problem classification; probability; selective pairwise classifiers; subset features; training datasets; tree augmented naive bayes; tree structure; Accuracy; Acoustics; Bandwidth; Bayesian methods; Emotion recognition; Feature extraction; Time frequency analysis; Detecting Emotion from Voice; Feature Selection; Pairwise Classification; Tree Augmented Naive Bayes;
Conference_Titel :
Computers & Informatics (ISCI), 2011 IEEE Symposium on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-1-61284-689-7
DOI :
10.1109/ISCI.2011.5958890