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

Enhancing Child Vocalization Classification in Multi-Channel Child-Adult Conversations Through Wav2vec2 Children ASR Features

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that often emerges in early childhood. ASD assessment typically involves an observation protocol including note-taking and ratings of child's social behavior conducted by a trained clinician. A robust machine learning (ML) model that is capable of labeling adult and child audio has the potential to save significant time and labor in manual coding children's behaviors. This may assist clinicians capture events of interest, better communicate events with parents, and educate new clinicians. In this study, we leverage the self-supervised learning model, Wav2Vec 2.0 (W2V2), pretrained on 4300h of home recordings of children under 5 years old, to build a unified system that performs both speaker diarization (SD) and vocalization classification (VC) tasks. We apply this system to two-channel audio recordings of brief 3-5 minute clinician-child interactions using the Rapid-ABC corpus. We propose a novel technique by introducing auxiliary features extracted from W2V2-based automatic speech recognition (ASR) system for children under 4 years old to improve children's VC task. We test our proposed method of improving children's VC task on two corpora (Rapid-ABC and BabbleCor) and observe consistent improvements. Furthermore, we reach, or perhaps outperform, the state-of-the-art performance of BabbleCor.Comment: Submitted to ICASSP 202

Analysis of atypical prosodic patterns in the speech of people with Down syndrome

Producción CientíficaThe speech of people with Down syndrome (DS) shows prosodic features which are distinct from those observed in the oral productions of typically developing (TD) speakers. Although a different prosodic realization does not necessarily imply wrong expression of prosodic functions, atypical expression may hinder communication skills. The focus of this work is to ascertain whether this can be the case in individuals with DS. To do so, we analyze the acoustic features that better characterize the utterances of speakers with DS when expressing prosodic functions related to emotion, turn-end and phrasal chunking, comparing them with those used by TD speakers. An oral corpus of speech utterances has been recorded using the PEPS-C prosodic competence evaluation tool. We use automatic classifiers to prove that the prosodic features that better predict prosodic functions in TD speakers are less informative in speakers with DS. Although atypical features are observed in speakers with DS when producing prosodic functions, the intended prosodic function can be identified by listeners and, in most cases, the features correctly discriminate the function with analytical methods. However, a greater difference between the minimal pairs presented in the PEPS-C test is found for TD speakers in comparison with DS speakers. The proposed methodological approach provides, on the one hand, an identification of the set of features that distinguish the prosodic productions of DS and TD speakers and, on the other, a set of target features for therapy with speakers with DS.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (grant TIN2017-88858-C2-1-R)Junta de Castilla y León (grant VA050G18

Automatic Classification of Autistic Child Vocalisations: A Novel Database and Results

Humanoid robots have in recent years shown great promise for supporting the educational needs of children on the autism spectrum. To further improve the efficacy of such interactions, user-adaptation strategies based on the individual needs of a child are required. In this regard, the proposed study assesses the suitability of a range of speech-based classification approaches for automatic detection of autism severity according to the com- monly used Social Responsiveness Scale ™ second edition (SRS- 2). Autism is characterised by socialisation limitations including child language and communication ability. When compared to neurotypical children of the same age these can be a strong indi- cation of severity. This study introduces a novel dataset of 803 utterances recorded from 14 autistic children aged between 4 – 10 years, during Wizard-of-Oz interactions with a humanoid robot. Our results demonstrate the suitability of support vector machines (SVMs) which use acoustic feature sets from multiple Interspeech C OM P AR E challenges. We also evaluate deep spec- trum features, extracted via an image classification convolutional neural network (CNN) from the spectrogram of autistic speech instances. At best, by using SVMs on the acoustic feature sets, we achieved a UAR of 73.7 % for the proposed 3-class task

Reduced volume of the arcuate fasciculus in adults with high-functioning autism spectrum conditions

Atypical language is a fundamental feature of autism spectrum conditions (ASC), but few studies have examined the structural integrity of the arcuate fasciculus, the major white matter tract connecting frontal and temporal language regions, which is usually implicated as the main transfer route used in processing linguistic information by the brain. Abnormalities in the arcuate have been reported in young children with ASC, mostly in low-functioning or non-verbal individuals, but little is known regarding the structural properties of the arcuate in adults with ASC or, in particular, in individuals with ASC who have intact language, such as those with high-functioning autism or Asperger syndrome. We used probabilistic tractography of diffusion-weighted images (DWI) to isolate and scrutinise the arcuate in a mixed-gender sample of 18 high-functioning adults with ASC (17 Asperger syndrome) and 14 age- and IQ-matched typically-developing controls. Arcuate volume was significantly reduced bilaterally with clearest differences in the right hemisphere. This finding remained significant in an analysis of all male participants alone. Volumetric reduction in the arcuate was significantly correlated with the severity of autistic symptoms as measured by the Autism-Spectrum Quotient. These data reveal that structural differences are present even in high-functioning adults with ASC, who presented with no clinically manifest language deficits and had no reported developmental language delay. Arcuate structural integrity may be useful as an index of ASC severity and thus as a predictor and biomarker for ASC. Implications for future research are discussed

Corpus callosum in preschoolers with Autism Spectrum Disorder: an imaging study

The hypothesis of abnormal neural connectivity, involving short- and long-distance connections, is one of the most sustained pathophysiological theories of ASD. Recently, whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature, because both were detected, although hypoconnectivity seems to dominate, particularly for corticocortical and interhemispheric functional connectivity. CC is the largest WM structure in human brain and it is the main connection and information transfer structure involved in interhemispheric communication. A growing body of literature has identified size reductions of the CC in subjects with ASD, and CC size also appears to be inversely related to autism severity and the intelligence quotient (IQ). However to date very few studies have been conducted on preschool age, when the disorder show its higher clinical expression. The main goal of our study is to compare the CC volume between preschoolers with ASD and controls subjects. We analyzed CC subregions volume in both groups also. Then, callosal size relations to demographic and clinical variables of ASD and control group (gender, age, non-verbal IQ, and language) have been examined. Lastly, in the ASD group we assessed callosal volume relationship with autism severity

Autism spectrum Disorder detection Using Face Features based on Deep Neural network

The majority of screening instruments for autism spectrum disorder (ASD) rely on subjective questions given to caregivers. Although behavioral observation is more objective, it is also more expensive, takes longer to complete, and requires a high level of competence. Therefore, there is still a dire need to create workable, scalable, and trustworthy systems that can identify ASD risk behaviors. Since there are no known causes of autism, early detection and intense therapy can significantly alter the behavior of children and people with the disorder. Artificial intelligence has made this possible, saving many lives in the process. Utilizing biological pictures, autism spectrum disorder (ASD) can be defined as a mental illness type which can be identified. The neurological condition known as ASD is linked to brain development and affects later appearance of the flask framework, a convolutional neural network (CNN) with transfer learning, and physical impression of the face. Xception, Visual Geometry Group Network (VGG16) the classification job was carried out using the previously trained models. 2,940 face photos made up the dataset utilized for the testing of those models, which was obtained via Kaggle platform. Outputs of the 3 models of deep learning have been evaluated with the use of common measures of assessment, including accuracy, sensitivity and specificity. With a 91% accuracy rate, Xception model had the greatest results. And theVGG16 models came next with (75%)

Design of a robotic toy and user interfaces for autism spectrum disorder risk assessment

Autism Spectrum Disorder (ASD) is an umbrella term for a spectrum of complex developmental disorders resulting in deficits in social communication and repetitive and stereotyped behaviors. According to research conducted in 2014, one in every 68 children in the United States is diagnosed with ASD. Despite this observation, there is no national screening system in Turkey, and screenings are not conducted systematically. Research in the area revealed that individuals with ASD are more interested in interact with technology (e.g. computers, iPad, robots, etc.) than human beings. This thesis includes research into how to design and use technology to create suitable products for deficits of ASD. With all of the concern over the high prevalence ratios of ASD, this thesis presents the methodology and design of a risk assessment device, which aims to capture the interest of children with ASD aged 3-4, and direct children who score low on the tests towards a diagnosis. In particular, the tests in the device focus on Theory of Mind (ToM) development and designed to detect differences with ToM tests between ASD and Typically Developing (TD) children. In the scope of the thesis, 2D illustrations, interface design, and outer shell design of the device are created in compliance with the research data in the field. Finally, outer shell design is 3D printed, and then surface is sanded and spray-painted