Name
Abstract | ||
|---|---|---|
Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TASL.2011.2112351 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
annotated time alignment,detecting mild cognitive impairment,clinical subject group,healthy elderly subject,present classification result,present result,automatic detection,automatic parses,spoken language derived measures,neuropsychological exam,neuropsychological test score,significant difference,neurophysiology,cognition,biomedical research,pragmatics,speech,parsing,psychology,natural language processing,linguistic complexity,bioinformatics | Neuropsychological test,Computer science,Mild cognitive impairment (MCI),Speech recognition,Linguistic sequence complexity,Natural language processing,Artificial intelligence,Cognition,Recall,Syntax,Spoken language,Neuropsychology | Journal |
Volume | Issue | ISSN |
19 | 7 | 1558-7916 |
Citations | PageRank | References |
50 | 2.79 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. Roark | 1 | 56 | 3.82 |
| Margaret Mitchell | 2 | 1450 | 65.37 |
| John-Paul Hosom | 3 | 50 | 2.79 |
| Kristy Hollingshead | 4 | 206 | 18.44 |
| Jeffrey Kaye | 5 | 131 | 13.09 |