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.

A. Kain, A. Amano-Kusumoto, and J.-P. Hosom, “Hybridizaing conversational and clear speech to determine the degree of contribution of acoustic features to intelligibility,” Journal of the Acoustical Society of America, vol. 124, no. 4, pp. 2308–2319, 2008

@inproceedings{amanokusumoto09_interspeech,
  title     = {{Classifying clear and conversational speech based on acoustic features}},
  author    = {Akiko Amano-Kusumoto and John-Paul Hosom and Izhak Shafran},
  year      = {2009},
  booktitle = {{Interspeech 2009}},
  pages     = {1735--1738},
  doi       = {10.21437/Interspeech.2009-522},
  issn      = {2958-1796},
}