Spoken language derived measures for detecting mild cognitive impairment

Brian Roark, Margaret Mitchell, John Paul Hosom, Kristy Hollingshead, Jeffrey Kaye

Research output: Contribution to journalArticle

129 Scopus citations

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.

Original languageEnglish (US)
Article number5710404
Pages (from-to)2081-2090
Number of pages10
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume19
Issue number7
DOIs
StatePublished - 2011

Keywords

  • Forced alignment
  • linguistic complexity
  • mild cognitive impairment (MCI)
  • parsing
  • spoken language understanding

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

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