Automatic detection of Mild cognitive impairment from spontaneous speech using ASR

László Tóth, Gábor Gosztolya, Veronika Vincze, Ildikó Hoffmann, Gréta Szatlóczki, Edit Biró, Fruzsina Zsura, Magdolna Pákáski, János Kálmán

Research output: Contribution to journalConference article

38 Citations (Scopus)

Abstract

Mild Cognitive Impairment (MCI), sometimes regarded as a prodromal stage of Alzheimer's disease, is a mental disorder that is difficult to diagnose. However, recent studies reported that MCI causes slight changes in the speech of the patient. Our starting point here is a study that found acoustic correlates of MCI, but extracted the proposed features manually. Here, we automate the extraction of the features by applying automatic speech recognition (ASR). Unlike earlier authors, we use ASR to extract only a phonetic level segmentation and annotation. While the phonetic output allows the calculation of features like the speech rate, it avoids the problems caused by the agrammatical speech frequently produced by the targeted patient group. Furthermore, as hesitation is the most important indicator of MCI, we take special care when handling filled pauses, which usually correspond to hesitation. Using the ASR-based features, we employ machine learning methods to separate the subjects with MCI from the control group. The classification results obtained with ASR-based feature extraction are just slightly worse that those got with the manual method. The F1 value achieved (85.3) is very promising regarding the creation of an automated MCI screening application.

Original languageEnglish
Pages (from-to)2694-2698
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2015-January
Publication statusPublished - 2015
Event16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany
Duration: Sep 6 2015Sep 10 2015

Keywords

  • Machine learning
  • Mild cognitive impairment
  • Temporal parameters of speech

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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