Extraction and Classification of Acoustic Features from Italian Speaking Children with Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of complex developmental conditions whose effects and severity show high intraindividual variability. However, one of the main symptoms shared along the spectrum is social interaction impairments that can be explored through acoustic analysis of speech production. In this paper, we compare 14 Italian-speaking children with ASD and 14 typically developing peers. Accordingly, we extracted and selected the acoustic features related to prosody, quality of voice, loudness, and spectral distribution using the parameter set eGeMAPS provided by the openSMILE feature extraction toolkit. We implemented four supervised machine learning methods to evaluate the extraction performances. Our findings show that Decision Trees (DTs) and Support Vector Machines (SVMs) are the best-performing methods. The overall DT models reach a 100% recall on all the trials, meaning they correctly recognise autistic features. However, half of its models overfit, while SVMs are more consistent. One of the results of the work is the creation of a speech pipeline to extract Italian speech biomarkers typical of ASD by comparing our results with studies based on other languages. A better understanding of this topic can support clinicians in diagnosing the disorder

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients

Computational Language Assessment in patients with speech, language, and communication impairments

Speech, language, and communication symptoms enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression. Nevertheless, traditional manual neurologic assessment, the speech and language evaluation standard, is time-consuming and resource-intensive for clinicians. We argue that Computational Language Assessment (C.L.A.) is an improvement over conventional manual neurological assessment. Using machine learning, natural language processing, and signal processing, C.L.A. provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. ii. facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and iii. allows easier extensibility to assess patients from a wide range of languages. Also, C.L.A. employs Artificial Intelligence models to inform theory on the relationship between language symptoms and their neural bases. It significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite

Automated profiling of spontaneous speech in primary progressive aphasia and behavioral-variant frontotemporal dementia: An approach based on usage-frequency

Language production provides important markers of neurological health. One feature of impairments of language and cognition, such as those that occur in stroke aphasia or Alzheimer's disease, is an overuse of high frequency, “familiar” expressions. We used computerized analysis to profile narrative speech samples from speakers with variants of frontotemporal dementia (FTD), including subtypes of primary progressive aphasia (PPA). Analysis was performed on language samples from 29 speakers with semantic variant PPA (svPPA), 25 speakers with logopenic variant PPA (lvPPA), 34 speakers with non-fluent variant PPA (nfvPPA), 14 speakers with behavioral variant FTD (bvFTD) and 20 older normal controls (NCs). We used frequency and collocation strength measures to determine use of familiar words and word combinations. We also computed word counts, content word ratio and a combination ratio, a measure of the degree to which the individual produces connected language. All dementia subtypes differed significantly from NCs. The most discriminating variables were word count, combination ratio, and content word ratio, each of which distinguished at least one dementia group from NCs. All participants with PPA, but not participants with bvFTD, produced significantly more frequent forms at the level of content words, word combinations, or both. Each dementia group differed from the others on at least one variable, and language production variables correlated with established behavioral measures of disease progression. A machine learning classifier, using narrative speech variables, achieved 90% accuracy when classifying samples as NC or dementia, and 59.4% accuracy when matching samples to their diagnostic group. Automated quantification of spontaneous speech in both language-led and non-language led dementias, is feasible. It allows extraction of syndromic profiles that complement those derived from standardized tests, warranting further evaluation as candidate biomarkers. Inclusion of frequency-based language variables benefits profiling and classification

Adaptation of Speaker and Speech Recognition Methods for the Automatic Screening of Speech Disorders using Machine Learning

This PhD thesis presented methods for exploiting the non-verbal communication of individuals suffering from specific diseases or health conditions aiming to reach an automatic screening of them. More specifically, we employed one of the pillars of non-verbal communication, paralanguage, to explore techniques that could be utilized to model the speech of subjects. Paralanguage is a non-lexical component of communication that relies on intonation, pitch, speed of talking, and others, which can be processed and analyzed in an automatic manner. This is called Computational Paralinguistics, which can be defined as the study of modeling non-verbal latent patterns within the speech of a speaker by means of computational algorithms; these patterns go beyond the linguistic} approach. By means of machine learning, we present models from distinct scenarios of both paralinguistics and pathological speech which are capable of estimating the health status of a given disease such as Alzheimer's, Parkinson's, and clinical depression, among others, in an automatic manner

Automatic speech analysis to early detect functional cognitive decline in elderly population

This study aimed at evaluating whether people with a normal cognitive function can be discriminated from subjects with a mild impairment of cognitive function based on a set of acoustic features derived from spontaneous speech. Voice recordings from 90 Italian subjects (age >65 years; group 1: 47 subjects with MMSE>26; group 2: 43 subjects with 20≤ MMSE ≤26) were collected. Voice samples were processed using a MATLAB-based custom software to derive a broad set of known acoustic features. Linear mixed model analyses were performed to select the features able to significantly distinguish between groups. The selected features (% of unvoiced segments, duration of unvoiced segments, % of voice breaks, speech rate, and duration of syllables), alone or in addition to age and years of education, were used to build a learning-based classifier. The leave-one-out cross validation was used for testing and the classifier accuracy was computed. When the voice features were used alone, an overall classification accuracy of 0.73 was achieved. When age and years of education were additionally used, the overall accuracy increased up to 0.80. These performances were lower than the accuracy of 0.86 found in a recent study. However, in that study the classification was based on several tasks, including more cognitive demanding tasks. Our results are encouraging because acoustic features, derived for the first time only from an ecologic continuous speech task, were able to discriminate people with a normal cognitive function from people with a mild cognitive decline. This study poses the basis for the development of a mobile application performing automatic voice analysis on-the-fly during phone calls, which might potentially support the detection of early signs of functional cognitive decline