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.

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.

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Language impairments in Alzheimer' s disease may appear at the prodromal stage. The most significant impairment is found at the lexical-semantic process level, which is explained either by a degradation of the areas that store the semantic network or by a failure at retrieving the information from that network. Regardless of the retrieval failure happening, there is evidence of the degradation of the semantic network at some levels. Several studies support the bottom-up breakdown, according to which the loss starts at the specific concept attribute level, along with the link with its coordinates, while superordinates are preserved. Some characteristics can affect this loss such as familiarity, age of acquisition, frequency, or affective features. While classic studies have focused on concrete neutral nouns, recent research is exploring the role of emotion. Since emotional processes strengthen the semantic relationship between concepts, it could be a relevant dimension for the preservation of the semantic network.

Language impairments in Alzheimer's disease (AD) primarily affect lexical and semantic levels, which significantly depend on the speaker's memory state. Qualitative shifts in semantic memory are due to neurodegenerative processes underlying dementia and give rise to anomia, or the speaker's inability to access and retrieve lexical units and their conceptual backgrounds. In this paper we aim at studying cognitive and semantic features of anomic deficit in AD and exploring the possibility to apply them in tests for early detection of dementia. For that purpose, we analyze the results from our experimental version of a classical test on semantic verbal fluency (SemVF) with nonpathological aged persons, persons with Mild Cognitive Impairment and persons with AD. The experimental version of the test introduces a division into four 15-minute intervals, in order to find out which processes of semantic access, either automatic or controlled, are impaired in different cognitive states of the elderly. Our results show a correlation between the cognitive state and the lexical-semantic ability of the speaker. Furthermore, they highlight, on the one hand, that the duration of the SemVF test should not be less than 60 seconds, with internal division into 4 15-second intervals, and, on the other hand, that some semantic categories-colours and fruit in particularare more prone to be affected by cognitive retrogenesis, which caracterizes AD dementia.

IntroductionIn this study we intend to use speech analysis to analyze the cognitive impairments caused by pathologies of vascular origin such as diabetes, hypertension, hypercholesterolemia and heart disease, predictors of the development of vascular dementia.MethodsIn this study, 40 participants with mild cognitive impairment were asked to read while being recorded and they were inquired about their history of the aforementioned conditions. Their speech was then analyzed.ResultsWe found that some speech parameters of frequencies and syllabic rhythm vary due to these pathologies. In addition, we conducted a discriminant analysis in which we found that diabetes and hypertension can be predicted with an accuracy over 95% with few speech parameters, and hypercholesterolemia and heart disease with an accuracy over 80%.DiscussionThe predictor parameters found are heterogeneous, including voice quality, amplitude, frequency, and rhythm parameters. This result may lead to investigate why such important qualitative changes occur in the voice of older adults with these pathologies. Rather than trying to find a diagnostic procedure already existing in classical medicine, we expect this finding to contribute to explore the causes and concomitant pathologies of these diseases. We discuss the implications of behavioral traits, such as speech, as digital biomarkers.

Background: During aging, changes in human speech may arise because of the neurophysiological deterioration associated with age, or as the result of an impairment in the cognitive processes underlying speech production. Some speech parameters show specific alterations under the presence of dementia. The objective of our study is to identify which of these parameters change because of age, cognitive state, or the interaction of both. Methods: The sample includes 400 people over 55 years old, who were divided into four groups, according to their age. The cognitive state of the participants was assessed through the MMSE test and three ranks were stablished. Gender was also considered in the analysis. Results: Certain temporal, fluency, rhythm, amplitude and voice quality parameters were found to be related to the cognitive state, while disturbance parameters changed due to age. Frequency parameters were exclusively influenced by gender. Conclusions: Understanding how speech parameters are specifically affected by age, cognitive state, or the interaction of both, is determinant to advance in the use of speech as a clinical marker for the detection of cognitive impairments.