Classifying clear and conversational speech based on acoustic features
Akiko Amano-Kusumoto, John-Paul Hosom, Izhak Shafran · 2009
This paper reports an investigation of features relevant for classifying two speaking styles, namely, conversational speaking style and clear (e.g. hyper-articulated) speaking style. Spectral and prosodic features were automatically extracted from speech and classified using decision tree classifiers and multilayer perceptrons to achieve accuracies of about 71 % and 77% respectively. More interestingly, we found that out of the 56 features only about 9 features are needed to capture the most predictive power. While perceptual studies have shown that spectral cues are more useful than prosodic features for intelligibility [1], here we find prosodic features are more important for classification. Index Terms: binary classification, acoustic features, decision tree classifier, multilayer perceptron,