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Publication Details

Learning Diagnostic Models Using Speech and Language Measures

by Peintner, B., Jarrold, W, Vergyri, D., Richey, C., Gorno Tempini, M., and Ogar, J.

30th Annual International IEEE EMBS Conference, August 20-24, Vancouver, British Columbia, Canada , 2008.

Abstract

We describe results that show the effectiveness of machine learning in the automatic diagnosis of certain neurodegenerative diseases, several of which alter speech and language production. We analyzed audio from 9 control subjects and 30 patients diagnosed with one of three subtypes of Frontotemporal Lobar Degeneration. From this data, we extracted features of the audio signal and the words the patient used, which were obtained using our automated transcription technologies. We then automatically learned models that predict the diagnosis of the patient using these features. Our results show that learned models over these features predict diagnosis with accuracy significantly better than random. Future studies using higher quality recordings will likely improve these results.

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AIC Computer Facilities

This project covers the aquisition and maintenance of the computers, network, and related facilities in the AIC

AIC Personnel

Name Title E-mail
Jarrold, William Alumnus
Peintner, Bart Sr. Computer Scientist

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