期刊
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
卷 19, 期 7, 页码 2081-2090出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASL.2011.2112351
关键词
Forced alignment; linguistic complexity; mild cognitive impairment (MCI); parsing; spoken language understanding
资金
- National Science Foundation (NSF) [IIS-0447214, BCS-0826654]
- National Institute of Health/National Institute of Aging (NIH/NIA) [P30AG08017, R01AG024059]
- Oregon Center for Aging and Technology (ORCATECH, NIH) [1P30AG024978-01]
- Oregon Partnership for Alzheimer's Research
- Division Of Behavioral and Cognitive Sci
- Direct For Social, Behav & Economic Scie [0826654] Funding Source: National Science Foundation
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.
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