Voice and Speech Features as a Diagnostic Symptom

Abstract

"The study of the peculiarities of speech of children with atypical development is necessary for the development of educational programs, children’s socialization and adaptation in society. The aim of this study is to determine the acoustic features of voice and speech of children with autism spectrum disorders (ASD) as a possible additional diagnostic criterion. The multiplicity of symptomatology, different age of its manifestation, and the presence of a leading symptom complex individually for each child make it difficult to diagnose ASD. To determine the specificity of speech features of ASD, we analyzed the speech of children with developmental disabilities in which speech disorders accompany the disease - Down syndrome (DS), intellectual disabilities (ID), mixed specific developmental disorders (MDD). The features that reflect the main physiological processes occurring in the speech tract during voice and speech production are selected for analysis. The speech of 300 children aged 4-16 years was analyzed. Speech files are selected from the speech database ""AD_Child.Ru"" (Lyakso et al., 2019). Acoustic features of voice and speech, which are specific for different developmental disorders, were determined. The speech of ASD children is characterized by: high pitch values (high voice); pitch variability; high values for the third formant (emotional) and its intensity causing ""atypical"" spectrogram of the speech signal; high values of vowel articulation index (VAI). The speech of children with DS is characterized by the maximal duration of vowels in words; low pitch values (low voice); a wide range of values of the VAI depending on the difficulty of speech material; low values of the third formant; unformed most of consonant phonemes. The characteristics of speech of children with ID are: high values of vowel’s duration in words, the pitch, and the third formant, low values of the VAI; of MDD - low pitch values and high values of the VAI. Based on the identified peculiarities specific to each disease, the set of acoustic features specific to ASD can be considered as a biomarker of autism and used as an additional diagnostic criterion. This will allow a timely diagnose, appoint treatment and develop individual programs for children. Speech characteristics of children with ID, DS, and MDD can be considered to a greater extent in the training and socialization of children and used in the development of training programs taking into account individual peculiarities of children."

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“…ML assists in the efficient management and analysis of biometric data during clinical trials, informing researchers and ensuring study outcomes' validity [199], [200]. • Voice and Speech Analysis for Diagnostics: Voice pattern analysis serves diagnostic purposes, detecting potential markers for various medical conditions [201], [202]. ML algorithms process voice and speech data to identify indications of conditions such as Parkinson's disease [203]- [205] or respiratory disorders [206], [207], contributing to diagnostic capabilities.…”