Aims: The study explores how speech measures may be linked to language profiles in participants with Alzheimer's disease (AD) and how these profiles could distinguish AD from changes associated with normal aging. Methods: We analysed simple sentences spoken by older adults with and without AD. Spectrographic analysis of temporal and acoustic characteristics was carried out using the Praat software. Results: We found that measures of speech, such as variations in the percentage of voice breaks, number of periods of voice, number of voice breaks, shimmer (amplitude perturbation quotient), and noise-to-harmonics ratio, characterise people with AD with an accuracy of 84.8%. Discussion: These measures offer a sensitive method of assessing spontaneous speech output in AD, and they discriminate well between people with AD and healthy older adults. This method of evaluation is a promising tool for AD diagnosis and prognosis, and it could be used as a dependent measure in clinical trials.

Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer's disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy.Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer's disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists.Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer's and 80% for mild cognitive impairment.Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer's disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.

Emotional states, attitudes and intentions are often conveyed by modulations in the tone of voice. Impaired recognition of emotions from a tone of voice (receptive prosody) has been described as characteristic symptoms of schizophrenia. However, the ability to express non-verbal information in speech (expressive prosody) has been understudied. This paper describes a useful technique for quantifying the degree of expressive prosody deficits in schizophrenia, using a semi-automatic method, and evaluates this method's ability to discriminate between patient and control groups. Forty-five medicated patients with a diagnosis of schizophrenia were matched with thirty-five healthy comparison subjects. Production of expressive prosodic speech was analyzed using variation in fundamental frequency (F0) measures on an emotionally neutral reading task. Results revealed that patients with schizophrenia exhibited significantly more pauses (p < .001), were slower (p < .001), and showed less pitch variability in speech (p < .05) and fewer variations in syllable timing (p < .001) than control subjects. These features have been associated with «flat» speech prosody. Signal processing algorithms applied to speech were shown to be capable of discriminating between patients and controls with an accuracy of 93.8%. These speech parameters may have a diagnostic and prognosis value and therefore could be used as a dependent measure in clinical trials.

The results show a direct relationship between the different acoustic parameters present in AD and the verbal fluency tests results.

This study explores several speech parameters related to mild cognitive impairment, as well as those that might be flagging the presence of an underlying neurodegenerative process. Speech is an excellent biomarker because it is not invasive and, what is more, its analysis is rapid and economical. Our aim has been to ascertain whether the typical speech patterns of people with Alzheimer’s disease are also present during the disorder’s preclinical stages. To do so, we shall be using a task that involves reading out aloud. This is followed by an analysis of the recordings, looking for the possible parameters differentiating between those older people with MCI and a high probability of developing dementia and those with MCI that will not do so. We found that the disease’s most differentiating parameters prior to its onset involve changes in speech duration and an alteration in rhythm rate and intensity. These parameters seem to be related to the first difficulties in lexical access among older people with AD.

This study broaches in a novel way the analysis of cognitive impairment characteristic of the early stages of Alzheimer's Disease (AD). Specifically, we attempt to determine the acoustic speech parameters that are sensitive to the onset of the disease, and their association with the language deficit characteristic of AD. Speech analysis was carried out on 21 elderly patients with AD using Praat software, which analyzes the acoustic components of speech. The data obtained were subjected to stepwise regression, using the overall scores obtained in the test as the criterion variable, and the scores on the frequency, amplitude and periodicity variables as predictors of performance. We found that the percentage of voiceless segments explains a significant portion of the variance in the overall scores obtained in the neuropsychological test. This component seems to be related mainly to the patient's ability in phonological fluency. This finding could permit the creation of a diagnostic test for AD through analysis of the acoustic speech parameters at very low cost in terms of both time and resources.

This device may be used at a preclinical stage by non-expert health professionals to determine the likelihood of the onset of AD.

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