Estimates of the prevalence of speech and motor speech disorders in persons with complex neurodevelopmental disorders (CND) can inform research in the biobehavioural origins and treatment of CND. The goal of this research was to use measures and analytics in a diagnostic classification system to estimate the prevalence of speech and motor speech disorders in convenience samples of speakers with one of eight types of CND. Audio-recorded conversational speech samples from 346 participants with one of eight types of CND were obtained from a database of participants recruited for genetic and behavioural studies of speech sound disorders (i.e., excluding dysfluency) during the past three decades. Data reduction methods for the speech samples included narrow phonetic transcription, prosody-voice coding, and acoustic analyses. Standardized measures were used to cross-classify participants' speech and motor speech status. Compared to the 17.8% prevalence of four types of motor speech disorders reported in a study of 415 participants with idiopathic Speech Delay (SD), 47.7% of the present participants with CND met criteria for one of four motor speech disorders, including Speech Motor Delay (25.1%), Childhood Dysarthria (13.3%), Childhood Apraxia of Speech (4.3%), and concurrent Childhood Dysarthria and Childhood Apraxia of Speech (4.9%). Findings are interpreted to indicate a substantial prevalence of speech disorders, and notably, a substantial prevalence of motor speech disorders in persons with some types of CND. We suggest that diagnostic classification information from standardized motor speech assessment protocols can contribute to research in the pathobiologies of CND. Abbreviations: 16p: 16p11.2 deletion and duplication syndrome; 22q: 22q11.2 deletion syndrome; ASD: Autism Spectrum Disorder; CAS: Childhood Apraxia of Speech; CD: Childhood Dysarthria; CND: Complex Neurodevelopmental Disorder; DS: Down syndrome; FXS: Fragile X syndrome; GAL: Galactosemia; IID: Idiopathic Intellectual Disability; MSD: Motor Speech Disorder; No MSD: No Motor Speech Disorder; NSA: Normal(ized) Speech Acquisition; PEPPER: Programs to Examine Phonetic and Phonologic Evaluation Records; PSD: Persistent Speech Delay; PSE: Persistent Speech Errors; SD: Speech Delay; SDCS: Speech Disorders Classification System; SDCSS: Speech Disorders Classification System Summary; SE: Speech Errors; SMD: Speech Motor Delay; SSD: Speech Sound Disorders; TBI: Traumatic Brain Injury.

Hyperkinetic dysarthria is characterized by abnormal involuntary movements affecting respiratory, phonatory, and articulatory structures impacting speech and deglutition. Speech-language pathologists (SLPs) play an important role in the evaluation and management of dysarthria and dysphagia. This review describes the standard clinical evaluation and treatment approaches by SLPs for addressing impaired speech and deglutition in specific hyperkinetic dysarthria populations.

Although there is substantial rationale for a motor component in the speech of persons with Down syndrome (DS), there presently are no published estimates of the prevalence of subtypes of motor speech disorders in DS. The goal of this research is to provide initial estimates of the prevalence of types of speech disorders and motor speech disorders in adolescents with DS. Conversational speech samples from a convenience sample of 45 adolescents with DS, ages 10 to 20 years old, were analysed using perceptual and acoustic methods and measures in the Speech Disorders Classification System (SDCS). The SDCS cross-classified participants into five mutually exclusive speech classifications and five mutually exclusive motor speech classifications. For participants meeting criteria for Childhood Dysarthria or for Childhood Dysarthria concurrent with Childhood Apraxia of Speech, the SDCS provided information on participants' percentile status on five subtypes of dysarthria. A total of 97.8% of participants met SDCS criteria for Speech Disorders and 97.8% met criteria for Motor Speech Disorders, including Childhood Dysarthria (37.8%), Speech Motor Delay (26.7%), Childhood Dysarthria and Childhood Apraxia of Speech (22.2%), and Childhood Apraxia of Speech (11.1%). Ataxia was the most prevalent dysarthria subtype. Nearly all participants with DS in the present sample had some type of speech and motor speech disorder, with implications for theory, assessment, prediction, and treatment. Specific to treatment, the present findings are interpreted as support for motor speech disorders as a primary explanatory construct to guide the selection and sequencing of treatment targets for persons with DS. Abbreviations: CAS: Childhood Apraxia of Speech; CD: Childhood Dysarthria; DS: Down syndrome; NSA: Normal(ized) Speech Acquisition; PSD: Persistent Speech Delay; PSE: Persistent Speech Errors; SD: Speech Delay; SDCS: Speech Disorders Classification System; SE: Speech Errors; SMD: Speech Motor Delay.

The employment of clinical databases in the study of mental disorders is essential to the diagnosis and treatment of patients with mental illness. While text corpora obtain merely limited information of content, speech corpora capture tones, emotions, rhythms and many other signals beyond content. Hence, the design and development of speech corpora for patients with mental disorders is increasingly important.

Currently, not all children that need speech therapy have access to a therapist. With the current international shortage of speech-language pathologists (SLPs), there is a demand for online tools to support SLPs with their daily tasks. Several online speech therapy (OST) systems have been designed and proposed in the literature; however, the implementation of these systems is lacking. The technical knowledge that is needed to use these programs is a challenge for SLPs. There has been limited effort to systematically identify, analyze and report the findings of prior studies. We provide the results of an extensive literature review of OST systems for childhood speech communication disorders. We systematically review OST systems that can be used in clinical settings or from home as part of a treatment program for children with speech communication disorders. Our search strategy found 4481 papers, of which 35 were identified as focusing on speech therapy programs for speech communication disorders. The features of these programs were examined, and the main findings are extracted and presented. Our analysis indicates that most systems which are designed mainly to support the SLPs adopt and use supervised machine learning approaches that are either desktop-based or mobile-phone-based applications. Our findings reveal that speech therapy systems can provide important benefits for childhood speech. A collaboration between computer programmers and SLPs can contribute to implementing useful automated programs, leading to more children having access to good speech therapy.

This study aimed to explore clinical correlates of repetitive speech disorders in patients with Parkinson's disease (PD).

There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine-learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders.

We examined narrative speech production longitudinally in non-demented (n=15) and mildly demented (n=8) patients with Parkinson's disease spectrum disorder (PDSD), and we related increasing impairment to structural brain changes in specific language and motor regions. Patients provided semi-structured speech samples, describing a standardized picture at two time points (mean±SD interval=38±24months). The recorded speech samples were analyzed for fluency, grammar, and informativeness. PDSD patients with dementia exhibited significant decline in their speech, unrelated to changes in overall cognitive or motor functioning. Regression analysis in a subset of patients with MRI scans (n=11) revealed that impaired language performance at Time 2 was associated with reduced gray matter (GM) volume at Time 1 in regions of interest important for language functioning but not with reduced GM volume in motor brain areas. These results dissociate language and motor systems and highlight the importance of non-motor brain regions for declining language in PDSD.

Considering the importance of auditory information for the acquisition and organization of phonological rules, the assessment of (central) auditory processing contributes to both the diagnosis and targeting of speech therapy in children with speech sound disorders.

Disorders of speech and language arise out of a complex interaction of genetic, environmental, and neural factors. Little is understood about the neural bases of these disorders. Here we systematically reviewed neuroimaging findings in Speech disorders (SD) and Language disorders (LD) over the last five years (2008-2013; 10 articles). In participants with SD, structural and functional anomalies in the left supramarginal gyrus suggest a possible deficit in sensory feedback or integration. In LD, cortical and subcortical anomalies were reported in a widespread language network, with little consistency across studies except in the superior temporal gyri. In summary, both functional and structural anomalies are associated with LD and SD, including greater activity and volumes relative to controls. The variability in neuroimaging approach and heterogeneity within and across participant samples restricts our full understanding of the neurobiology of these conditions- reducing the potential for devising novel interventions targeted at the underlying pathology.

Natural speech plays a pivotal role in communication and interactions between human beings. The prosody of natural speech, due to its high ecological validity and sensitivity, has been acoustically analyzed and more recently utilized in machine learning to identify individuals with autism spectrum disorders (ASDs). In this meta-analysis, we evaluated the findings of empirical studies on acoustic analysis and machine learning techniques to provide statistically supporting evidence for adopting natural speech prosody for ASD detection. Using a random-effects model, the results observed moderate-to-large pooled effect sizes for pitch-related parameters in distinguishing individuals with ASD from their typically developing (TD) counterparts. Specifically, the standardized mean difference (SMD) values for pitch mean, pitch range, pitch standard deviation, and pitch variability were 0.3528, 0.6744, 0.5735, and 0.5137, respectively. However, the differences between the two groups in temporal features could be unreliable, as the SMD values for duration and speech rate were only 0.0738 and -0.0547. Moderator analysis indicated task types were unlikely to influence the final results, whereas age groups showed a moderating role in pooling pitch range differences. Furthermore, promising accuracy rates on ASD identification were shown in our analysis of multivariate machine learning studies, indicating averaged sensitivity and specificity of 75.51% and 80.31%, respectively. In conclusion, these findings shed light on the efficacy of natural prosody in identifying ASD and offer insights for future investigations in this line of research.

A prototype of a tangible user interface (TUI) for a fishing game, which is intended to be used by children with speech sound disorders (SSD), speech and language therapists (SLTs), and kindergarten teachers and assistants (KTAs) and parents alike, has been developed and tested.

Mood disorders are commonly underrecognized and undertreated, as diagnosis is reliant on self-reporting and clinical assessments that are often not timely. Speech characteristics of those with mood disorders differs from healthy individuals. With the wide use of smartphones, and the emergence of machine learning approaches, smartphones can be used to monitor speech patterns to help the diagnosis and monitoring of mood disorders.

A longstanding notion in the concept of psychosis is the prominence of loosened associative links in thought processes. Assessment of such subtle aspects of thought disorders has proved to be a challenging task in clinical practice and to date no surrogate markers exist that can reliably track the physiological effects of treatments that could reduce thought disorders. Recently, automated speech graph analysis has emerged as a promising means to reliably quantify structural speech disorganization.

Delineating candidate genes at the chromosomal breakpoint regions in the apparently balanced chromosome rearrangements (ABCR) has been shown to be more effective with the emergence of next-generation sequencing (NGS) technologies. We employed a large-insert (7-11 kb) paired-end tag sequencing technology (DNA-PET) to systematically analyze genome of four patients harbouring cytogenetically defined ABCR with neurodevelopmental symptoms, including developmental delay (DD) and speech disorders. We characterized structural variants (SVs) specific to each individual, including those matching the chromosomal breakpoints. Refinement of these regions by Sanger sequencing resulted in the identification of five disrupted genes in three individuals: guanine nucleotide binding protein, q polypeptide (GNAQ), RNA-binding protein, fox-1 homolog (RBFOX3), unc-5 homolog D (C.elegans) (UNC5D), transmembrane protein 47 (TMEM47), and X-linked inhibitor of apoptosis (XIAP). Among them, XIAP is the causative gene for the immunodeficiency phenotype seen in the patient. The remaining genes displayed specific expression in the fetal brain and have known biologically relevant functions in brain development, suggesting putative candidate genes for neurodevelopmental phenotypes. This study demonstrates the application of NGS technologies in mapping individual gene disruptions in ABCR as a resource for deciphering candidate genes in human neurodevelopmental disorders (NDDs).

The 'sensitive period' for phonetic learning posits that between 6 and 12 months of age, infants' discrimination of native and nonnative speech sounds diverge. Individual differences in this dynamic processing of speech have been shown to predict later language acquisition up to 30 months of age, using parental surveys. Yet, it is unclear whether infant speech discrimination could predict longer-term language outcome and risk for developmental speech-language disorders, which affect up to 16 % of the population. The current study reports a prospective prediction of speech-language skills at a much later age-6 years-old-from the same children's nonnative speech discrimination at 11 months-old, indexed by MEG mismatch responses. Children's speech-language skills at 6 were comprehensively evaluated by a speech-language pathologist in two ways: individual differences in spoken grammar, and the presence versus absence of speech-language disorders. Results showed that the prefrontal MEG mismatch response at 11 months not only significantly predicted individual differences in spoken grammar skills at 6 years, but also accurately identified the presence versus absence of speech-language disorders, using a machine-learning classification. These results represent new evidence that advance our theoretical understanding of the neurodevelopmental trajectory of language acquisition and early risk factors for developmental speech-language disorders.

Medical diagnostic methods that utilise modalities of patient symptoms such as speech are increasingly being used for initial diagnostic purposes and monitoring disease state progression. Speech disorders are particularly prevalent in neurological degenerative diseases such as Parkinson's disease, the focus of the study undertaken in this work. We will demonstrate state-of-the-art statistical time-series methods that combine elements of statistical time series modelling and signal processing with modern machine learning methods based on Gaussian process models to develop methods to accurately detect a core symptom of speech disorder in individuals who have Parkinson's disease. We will show that the proposed methods out-perform standard best practices of speech diagnostics in detecting ataxic speech disorders, and we will focus the study, particularly on a detailed analysis of a well regarded Parkinson's data speech study publicly available making all our results reproducible. The methodology developed is based on a specialised technique not widely adopted in medical statistics that found great success in other domains such as signal processing, seismology, speech analysis and ecology. In this work, we will present this method from a statistical perspective and generalise it to a stochastic model, which will be used to design a test for speech disorders when applied to speech time series signals. As such, this work is making contributions both of a practical and statistical methodological nature.

Speech sound disorders (SSD) manifest as difficulties in phonological memory and awareness, oral motor function, language, vocabulary, reading, and spelling. Families enriched for SSD are rare, and typically display a cluster of deficits. We conducted a genome-wide association study (GWAS) in 435 children from 148 families in the Cleveland Family Speech and Reading study (CFSRS), examining 16 variables representing 6 domains. Replication was conducted using the Avon Longitudinal Study of Parents and Children (ALSPAC). We identified 18 significant loci (combined p < 10-8) that we pursued bioinformatically. We prioritized 5 novel gene regions with likely functional repercussions on neural pathways, including those which colocalized with differentially methylated regions in our sample. Polygenic risk scores for receptive language, expressive vocabulary, phonological awareness, phonological memory, spelling, and reading decoding associated with increasing clinical severity. In summary, neural-genetic influence on SSD is primarily multigenic and acts on genomic regulatory elements, similar to other neurodevelopmental disorders.

Individuals with autism spectrum disorder (ASD) commonly display speech processing abnormalities. Binding of acoustic features of speech distributed across different frequencies into coherent speech objects is fundamental in speech perception. Here, we tested the hypothesis that the cortical processing of bottom-up acoustic cues for speech binding may be anomalous in ASD. We recorded magnetoencephalography while ASD children (ages 7-17) and typically developing peers heard sentences of sine-wave speech (SWS) and modulated SWS (MSS) where binding cues were restored through increased temporal coherence of the acoustic components and the introduction of harmonicity. The ASD group showed increased long-range feedforward functional connectivity from left auditory to parietal cortex with concurrent decreased local functional connectivity within the parietal region during MSS relative to SWS. As the parietal region has been implicated in auditory object binding, our findings support our hypothesis of atypical bottom-up speech binding in ASD. Furthermore, the long-range functional connectivity correlated with behaviorally measured auditory processing abnormalities, confirming the relevance of these atypical cortical signatures to the ASD phenotype. Lastly, the group difference in the local functional connectivity was driven by the youngest participants, suggesting that impaired speech binding in ASD might be ameliorated upon entering adolescence.

Syntax, the grammatical structure of sentences, is a fundamental aspect of language. It remains debated whether reduced syntactic complexity is unique to schizophrenia spectrum disorder (SSD) or whether it is also present in major depressive disorder (MDD). Furthermore, the association of syntax (including syntactic complexity and diversity) with language-related neuropsychology and psychopathological symptoms across disorders remains unclear. Thirty-four SSD patients and thirty-eight MDD patients diagnosed according to DSM-IV-TR as well as forty healthy controls (HC) were included and tasked with describing four pictures from the Thematic Apperception Test. We analyzed the produced speech regarding its syntax delineating measures for syntactic complexity (the total number of main clauses embedding subordinate clauses) and diversity (number of different types of complex sentences). We performed cluster analysis to identify clusters based on syntax and investigated associations of syntactic, to language-related neuropsychological (verbal fluency and verbal episodic memory), and psychopathological measures (positive and negative formal thought disorder) using network analyses. Syntax in SSD was significantly reduced in comparison to MDD and HC, whereas the comparison of HC and MDD revealed no significant differences. No associations were present between speech measures and current medication, duration and severity of illness, age or sex; the single association accounted for was education. A cluster analysis resulted in four clusters with different degrees of syntax across diagnoses. Subjects with less syntax exhibited pronounced positive and negative symptoms and displayed poorer performance in executive functioning, global functioning, and verbal episodic memory. All cluster-based networks indicated varying degrees of domain-specific and cross-domain connections. Measures of syntactic complexity were closely related while syntactic diversity appeared to be a separate node outside of the syntactic network. Cross-domain associations were more salient in more complex syntactic production.