Acceptability of collecting speech samples from the elderly via the telephone

Abstract

Objective There is a critical need to develop rapid, inexpensive and easily accessible screening tools for mild cognitive impairment (MCI) and Alzheimer’s disease (AD). We report on the efficacy of collecting speech via the telephone to subsequently develop sensitive metrics that may be used as potential biomarkers by leveraging natural language processing methods. Methods Ninety-one older individuals who were cognitively unimpaired or diagnosed with MCI or AD participated from home in an audio-recorded telephone interview, which included a standard cognitive screening tool, and the collection of speech samples. In this paper we address six questions of interest: (1) Will elderly people agree to participate in a recorded telephone interview? (2) Will they complete it? (3) Will they judge it an acceptable approach? (4) Will the speech that is collected over the telephone be of a good quality? (5) Will the speech be intelligible to human raters? (6) Will transcriptions produced by automated speech recognition accurately reflect the speech produced? Results Participants readily agreed to participate in the telephone interview, completed it in its entirety, and rated the approach as acceptable. Good quality speech was produced for further analyses to be applied, and almost all recorded words were intelligible for human transcription. Not surprisingly, human transcription outperformed off the shelf automated speech recognition software, but further investigation into automated speech recognition shows promise for its usability in future work. Conclusion Our findings demonstrate that collecting speech samples from elderly individuals via the telephone is well tolerated, practical, and inexpensive, and produces good quality data for uses such as natural language processing.

An explainable machine learning model of cognitive decline derived from speech

“…Participants comprised 91 older individuals who were cognitively unimpaired ( n = 29, mean age = 72.48 [SD = 1.47], mean years of education = 18.00 [SD = 0.37], 41% female) or diagnosed with aMCI ( n = 32, mean age = 74.03 [SD = 1.01], mean years of education = 17.34 [SD = 0.30], 41% female) or mild AD ( n = 30, mean age = 74.93 [SD = 1.40], mean years of education = 16.68 [SD = 0.43], 52% female) 9,26 . The group demographics did not differ significantly ( p ‐values > 0.01).…”

Discovery and Analytical Validation of a Vocal Biomarker to Monitor Anosmia and Ageusia in Patients With COVID-19: Cross-sectional Study

“…Voice is a proven source of medical information, can be easily recorded on a large scale through smart devices [ 25 ], and can be easily used to build personalized corpora [ 26 ]. Studies have shown great results in the early diagnosis of neurological disorders such as Parkinson disease [ 27 , 28 ], Alzheimer disease [ 29 ], and mild cognitive impairment [ 30 , 31 ], since they directly alter the voice, but also in nonneurological conditions such as cardiometabolic [ 32 ] and pulmonary [ 33 ] diseases.…”

Using automated syllable counting to detect missing information in speech transcripts from clinical settings

“…Two different datasets were examined to illustrate the contrast between high- and low-quality audio recordings: (1) Telephone data - from a study assessing the cognitive state of older participants ( Diaz-Asper et al, 2021 ). Participants ( N = 91) were community dwelling, older English speakers (mean age = 73.7 ± 6.9 years of age) who had been called at home (on landlines, cellphones and speaker phones) and spoke for several minutes about past memorable experiences or hobbies.…”