Learning diagnostic models using speech and language measures

Bart Peintner, William Jarrold, Dimitra Vergyri, Colleen Richey, Maria Luisa Gorno Tempini, Jennifer Ogar
2008 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
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
more » ... y 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.
doi:10.1109/iembs.2008.4650249 pmid:19163752 fatcat:we3guradxzhddjgowadzw6opqe