Abstract
Speech signals may provide important information for measuring and modelling human behaviour, especially for assessing mental health as well as estimating the emotional state of a person. Speaker’s physiological and/or physical state may be thus identified by detecting the cognitive decline (CD) or stress levels using signal analysis of voice. This preliminary study presents a survey on methods introduced for CD and stress voice detection in humans. It is shown that increase in signal’s fundamental frequency (f0) as well as its frequency formants, are the most common effects of CD and stress. Additional voice parameters could be used to identify normal from CD and normal from stressed voice as well as the cognitive state of the elderly. The present study poses the initiation of a project for the development of a mobile application for the automated voice detection and analysis on the fly, which will aid in the detection of early signs of CD and stress. Further investigation with application on a large number of voice samples is required for validating the methods.
Highlights
The transitory step between physiological aging and dementia is known as mild cognitive impairment (MCI)
Additional voice parameters could be used to identify normal from cognitive decline (CD) and normal from stressed voice as well as the cognitive state of the elderly
In this paper we presented a survey on methods introduced so far in the current literature for stress voice detection in humans
Summary
The transitory step between physiological aging and dementia is known as mild cognitive impairment (MCI). A number of voice analysis studies ( presented in Table 1), have been proposed in the literature, for detecting voice under stress or CD in voice These includes, measuring, the overall intensity, Teager energy operator, Mel-Frequency Cepstral Coefficients, and the extractions of the functionals of f0 0. It was found in 0, that these features are very important parameters in the analysis of cognitive alterations It was shown, that some linguistic features may be used to discriminate between healthy and impaired elderly subjects (spectral centroid mean, statistics of voice signal, openings and closing voice intervals and f0-f5). Findings from the literature review are heterogeneous, but in almost all studies investigated, it was indicated that a consistent universal trend of an increase in f0 is demonstrated with the voice stress (see figure 1 and table 1) An explanation of this increase maybe stress-associated tensing of the musculature (cricothyroid muscle) 0. Further investigation with a large number of samples is required for validating the methods and for estimating reliable indicators of stress and voice evaluation metrics
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