Recognition of Negative Emotions from the Speech Signal (bibtex)
by Lee, C. M., Narayanan, Shrikanth and Pieraccin, R.
Abstract:
This paper reports on methods for automatic classification of spoken utterances based on the emotional state of the speaker. The data set used for the analysis comes from a corpus of human- machine dialogs recorded from a commercial application deployed by SpeechWorks. Linear discriminant classification with Gaussian class-conditional probability distribution and knearest neighborhood methods are used to classify utterances into two basic emotion states, negative and non-negative. The features used by the classifiers are utterance-level statistics of the fundamental frequency and energy of the speech signal. To improve classification performance, two specific feature selection methods are used; namely, promising first selection and forward feature selection. Principal component analysis is used to reduce the dimensionality of the features while maximizing classification accuracy. Improvements obtained by feature selection and PCA are reported in this paper. We reported the results.
Reference:
Recognition of Negative Emotions from the Speech Signal (Lee, C. M., Narayanan, Shrikanth and Pieraccin, R.), In Proceedings of Automatic Speech Recognition and Understanding Workshop (ASRU 2001), 2001.
Bibtex Entry:
@inproceedings{lee_recognition_2001,
	title = {Recognition of {Negative} {Emotions} from the {Speech} {Signal}},
	url = {http://ict.usc.edu/pubs/Recognition%20of%20Negative%20Emotions%20from%20the%20Speech%20Signal.pdf},
	abstract = {This paper reports on methods for automatic classification of spoken utterances based on the emotional state of the speaker. The data set used for the analysis comes from a corpus of human- machine dialogs recorded from a commercial application deployed by SpeechWorks. Linear discriminant classification with Gaussian class-conditional probability distribution and knearest neighborhood methods are used to classify utterances into two basic emotion states, negative and non-negative. The features used by the classifiers are utterance-level statistics of the fundamental frequency and energy of the speech signal. To improve classification performance, two specific feature selection methods are used; namely, promising first selection and forward feature selection. Principal component analysis is used to reduce the dimensionality of the features while maximizing classification accuracy. Improvements obtained by feature selection and PCA are reported in this paper. We reported the results.},
	booktitle = {Proceedings of {Automatic} {Speech} {Recognition} and {Understanding} {Workshop} ({ASRU} 2001)},
	author = {Lee, C. M. and Narayanan, Shrikanth and Pieraccin, R.},
	year = {2001}
}
Powered by bibtexbrowser