Analysis Of Spontaneous Speech Research Articles

Automated analysis of spoken language differentiates multiple system atrophy from Parkinson’s disease

Background and objectivesPatients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson’s disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD.MethodsSpontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing. A quantitative analysis was performed using 6 lexical and syntactic and 2 acoustic features. Results were compared with human-controlled analysis to assess the robustness of the approach. Diagnostic accuracy was evaluated using sensitivity analysis.ResultsDespite similar disease duration, linguistic abnormalities were generally more severe in MSA than in PD, leading to high diagnostic accuracy with an area under the curve of 0.81. Compared to controls, MSA showed decreased grammatical component usage, more repetitive phrases, shorter sentences, reduced sentence development, slower articulation rate, and increased duration of pauses, whereas PD had only shorter sentences, reduced sentence development, and longer pauses. Only slower articulation rate was distinctive for MSA while unchanged for PD relative to controls. The highest correlation was found between bulbar/pseudobulbar clinical score and sentence length (r = −0.49, p = 0.002). Despite the relatively high severity of dysarthria in MSA, a strong agreement between manually and automatically computed results was achieved.DiscussionAutomated linguistic analysis may offer an objective, cost-effective, and widely applicable biomarker to differentiate synucleinopathies with similar clinical manifestations.

A Non-invasive Approach for Early Alzheimer’s Detection Through Spontaneous Speech Analysis Using Deep Visibility Graphs

A Non-invasive Approach for Early Alzheimer’s Detection Through Spontaneous Speech Analysis Using Deep Visibility Graphs

Effects of Normal Cognitive Aging on Spoken Word Frequency: Older Adults Exhibit Higher Function Word Frequency and Lower Content Word Frequency than Young Adults

Background Recent work indicates that word frequency (WF), broadly defined as an estimate of how often a given word is produced during spontaneous speech, has been shown to be a sensitive marker for conditions like mild cognitive impairment (MCI) and Alzheimer’s disease (AD). WF has been linked to cognitive declines observed in these groups. However, it is largely unknown how WF for distinct word classes change as part of normative cognitive aging, and to what extent factors like education and life experience may protect against age-related cognitive decline. Objective The current study examined WF and its association with cognitive test performance in older and younger adults. Higher WF values indicate the use of more common, higher frequently used words that are more readily retrieved from the lexicon, whereas lower WF values indicate the use of less common, lower frequency words. Methods Expository speech transcripts of 70 older and 130 younger adults were analyzed. Subject-level WF indices were computed for all words (AWs), content words (CWs) and function words (FWs). Between-group differences in WF and neuropsychological test performance were examined using independent samples t-tests and associations between WF and cognitive performance were evaluated with Pearson correlations. Follow-up analyses clarified the possible contribution of education. Results Higher average AW and CW frequency was observed in younger adults and higher AW frequency was associated with poorer performance on a test of mental reasoning (Cognitive Estimation Test). Though small, differences between age groups remained significant after accounting for education levels. Conclusion The current results show generally preserved WF in older adults and suggest that healthy cognitive aging and formal education do not drive the age-related changes in WF found in other studies. If replicated, such findings suggest that a shift from lower to higher frequency words in the spontaneous speech of older adults may be a marker of early neuropathological changes rather than normative cognitive aging and that the specific pattern may be influenced by sociocultural factors like language exposure and life experience. Future studies examining changes in lexical retrieval with advancing age will help clarify the impact of normative cognitive aging on WF indices and the extent to which analysis of spontaneous speech may help with early detection for conditions like MCI and AD.

Politeness Pragmatic Orientation in the Production of Advice-giving Speech Acts in Arab TV Talent Shows

Identifying and understanding the distinctive features of speech acts’ realization across different cultures is essential for the pragmatic profiling of any language variety. Existing literature on advice-giving speech acts in Arabic predominantly examines religious texts composed in classical Arabic, overlooking everyday utterances spoken in contemporary Arabic dialects. Although Arabic has many varieties across different cultures, there are some shared values and beliefs that connect various Arab speech communities. Consequently, certain generalizations regarding acceptable politeness patterns in the Arab culture may be established, while acknowledging the presence of cultural and linguistic peculiarities. This study offers a descriptive analysis of spontaneous speech, emphasizing the politeness patterns delineated by Leech (2014), in the context of advice-giving in the Arab culture. The data of this study was obtained from ‘Boulevard Almawaheb’ talent show, comprising a total of seventy-nine advice-giving acts. The analysis followed the mixed-method design, mainly applying qualitative content analysis, supported by quantitative data. The findings indicate a preference for on-record indirect strategies over direct strategies and hints, along with a tendency to prioritize certain constraints, such as Approbation. The present study seeks to address the research gap in advice-giving pragmatic orientation within the Arab culture, with the intention of contributing to the establishment of a comprehensive pragmatic profile of speech acts in the Arab world.

Spontaneous speech: a robust measurement before, during and after awake brain surgery in patients with glioma

Background Patients with glioma often report language complaints with devastating effect on daily life. Analysing spontaneous speech can help to understand underlying language problems. Spontaneous speech monitoring is also of importance during awake brain surgery: it can guide tumour resection and contributes to maintaining language function. We aimed to investigate the spontaneous speech of patients with glioma in the perioperative period and the additional value of spontaneous speech analyses compared to standardised language testing. Methods We elicited and transcribed spontaneous speech of eight patients with glioma elected for awake brain surgery preoperatively, intraoperatively and 2.0–3.5 months postoperatively. Linguistic errors were coded. Type Token Ratio, Mean Length of Utterance of words, minimal utterances, and errors were extracted from the transcriptions. Patients were categorised based on total error patterns: stable, decrease or increase during surgery. Reliable Change Index scores were calculated for all spontaneous speech variables to objectify changes between time points. Language performance on language tests was compared to spontaneous speech variables. Results Most errors occurred in lexico-syntax, followed by phonology/articulation, syntax, and semantics. The predominant errors were Repetitions, Self-corrections, and Incomplete sentences. Most patients remained stable over time in almost all spontaneous speech variables, except in Incomplete sentences, which deteriorated in most patients postoperatively compared to intraoperatively. Some spontaneous speech variables (total errors, MLUw, TTR) gave more information on language change than a standard language test. Conclusions While the course of spontaneous speech over time remained relatively stable in most patients, Incomplete sentences seems to be a robust marker of language difficulties patients with glioma. These errors can be prioritised in spontaneous speech analysis to save time, especially to determine intra- to postoperative deterioration. Importantly, spontaneous speech analyses can give more information on language change than standardised language testing and should therefore be used in addition to standardised language tests.

Natural Language Processing Applied to Spontaneous Recall of Famous Faces Reveals Memory Dysfunction in Temporal Lobe Epilepsy Patients.

Epilepsy patients rank memory problems as their most significant cognitive comorbidity. Current clinical assessments are laborious to administer and score and may not always detect subtle memory decline. The Famous Faces Task (FF) has robustly demonstrated that left temporal lobe epilepsy (LTLE) patients remember fewer names and biographical details compared to right TLE (RTLE) patients and healthy controls (HCs). We adapted the FF task to capture subjects' entire spontaneous spoken recall, then scored responses using manual and natural language processing (NLP) methods. We expected to replicate previous group level differences using spontaneous speech and semi-automated analysis. Seventy-three (N=73) adults (28 LTLE, 18 RTLE, and 27 HCs) were included in a case-control prospective study design. Twenty FF in politics, sports, and entertainment (active 2008-2017) were shown to subjects, who were asked if they could recognize and spontaneously recall as much biographical detail as possible. We created human-generated and automatically-generated keyword dictionaries for each celebrity, based on a randomly selected training set of half of the HC transcripts. To control for speech output, we measured the speech duration, total word count and content word count for the FF task and a Cookie Theft Control Task (CTT), in which subjects were merely asked to describe a visual scene. Subjects' responses to FF and CTT tasks were recorded, transcribed, and analyzed in a blinded manner with a combination of manual and automated NLP approaches. Famous face recognition accuracy was similar between groups. LTLE patients recalled fewer biographical details compared to HCs and RTLEs using both the gold-standard human-generated dictionary (24%±12% vs. 31%±12% and 30%±12%, p=0.007) and the automated dictionary (24%±12% vs. 31%±12% and 32%±13%, p=0.007). There were no group level differences in speech duration, total word count, or content word count for either the FF and CTT to explain difference in recall performance. There was a positive, statistically significant relationship between MOCA score and FF recall performance as scored by the human-generated (ρ= .327, p= .029) and automatically-generated dictionaries (ρ= .422, p= .004) for TLE subjects, but not HCs, an effect that was driven by LTLE subjects. LTLE patients remember fewer details of famous people than HCs or RTLE patients, as discovered by NLP analysis of spontaneous recall. Decreased biographical memory was not due to decreased speech output and correlated with lower MOCA scores. NLP analysis of spontaneous recall can detect memory dysfunction in clinical populations in a semi-automated, objective, and sensitive manner.

Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study.

The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults. This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline. A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability. In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets. This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature.

Tempo-rhythmic characteristics of spontaneous speech of speakers of certain types of mental accentuations of personality in states of psychoemotional tension

The subject of psycholinguistic research is the formal signs of spontaneous sounding speech of the speaker in conditions of emotional tension. In our opinion, indicators of emotional tension of speech in perception are, first of all, formal means of sounding speech at the level of rhythm, tempo, speech failures, which are in relation to complementarity with the semantic content of the phrase and signal the degree of tension of speech. Thus, the state of emotional tension is transmitted by a complex of formal signs of sounding speech. As an example of the psychological factor of emotional tension, we considered court hearings in criminal cases. The object was audio recordings of spontaneous sounding speech of native speakers of the Russian language in a situation that determines the state of emotional tension of the speaker. The novelty of the research lies in the use of qualitative experimental methods in the analysis of spontaneous sounding speech in a situation of emotional tension. Obtaining objective results becomes possible on the basis of auditory linguistic analysis, which allows with a certain degree of reliability to assess the emotional state of the speaker (deviation from the norm, anxiety, tension, the presence and type of emotions, for example, excitement, surprise, fear / anger, etc.), psychophysiological state (abnormality, the presence of pathology), finally, establish boundary criteria for determining a stress disorder for a particular mental type. Of particular interest is the appeal to the psychological typologization of the personality of K. Leonhard, which makes a reliable analysis not only of the characteristics of the character (the so-called psychological phenotype of the personality), but also of his temperament (the psychological basis of the personality).

The Importance of Studying Spontaneous Speech in Computational Linguistics

This scientific work provides information on the importance of studying spontaneous speech in computational linguistics. Studying spontaneous speech has numerous practical implications. The ramifications of spontaneous speech analysis are extensive, ranging from improving voice assistants and speech-to-text systems to enhancing human-computer interaction. An examination of spontaneous speech in computational linguistics offers a more authentic depiction of language usage, poses difficulties for current models, and opens up fresh opportunities for enhancing the precision and adaptability of language processing systems. The integration of spontaneous speech analysis will be crucial in developing the discipline of computational linguistics as technology progresses.

The Use of the Teddy Talk Test to Determine Speech and Language Development in Children Aged 4 to 5 Years

<p style="text-align: justify;">Speech therapy uses a combination of approaches to assess speech and language, including analysis of spontaneous speech, use of standardized scales and assessment by the child’s parents and/or preschool teachers. In this paper, we present the Teddy Talk Test. Using a sample of sixteen children aged 4 to 5 years, we examined whether the test provided representative data on child language development. The parent questionnaire included information on the parents’ education, the parents’ native language, the child’s gender and the language the parents speak with their child. It was found that the Teddy Talk Test has the characteristics of screening tests for speech and language: it is time-efficient, it covers receptive and expressive language tasks, and it allows for rapid assessment of speech and language in large numbers of children. However, since the Teddy Talk Test has not yet been evaluated, we do not know its value regarding sensitivity, specificity and predictive validity.</p>

2 Reading Aloud Elicits Connected Speech and Autocorrection: a Novel Marker of Alzheimer’s Disease and Risk

Objective:Spontaneous speech involves tight coordination of a constellation of cognitive mechanisms (including planning, lexical selection, grammatical encoding, internal & external monitoring). Recent years brought a flurry of interest in detailed analysis of spontaneous speech in search of markers of prodromal Alzheimer’s disease. This work dates back to the nun studies by Snowdon et al (1996) and reveals promise for early detection through identification of subtle but significant changes in the nature of speech output years prior to diagnosis of dementia. A major challenge for neuropsychology is to develop methods to harness the potential sensitivity of language to subtle cognitive changes when testing individuals in clinical settings. In this talk I will present two lines of research that illustrate how reading aloud can be used to engage the cognitive mechanisms of spontaneous speech production in a manner that provides an easily accessible measure of Alzheimer’s disease/risk.Participants and Methods:In the first study, Spanish-English bilinguals with mild-to-moderate Alzheimer’s disease (n=20) and proficiency matched controls (n=29) read aloud mixed-language paragraphs with a small number of language-switched words, and we recorded the number of times they automatically translated switch words by accident (e.g., saying pero instead of but; effectively autocorrecting language switches to avoid producing switches overtly). In the second study, cognitively normal monolinguals at risk for AD based on CSF biomarkers (n=14) and controls (n=50) read aloud short paragraphs in which ten critical target words were replaced with autocorrect targets (e.g., The player who scored that final [paint] for the local team reported [him] experience). Participants were instructed to avoid autocorrecting (e.g., avoid saying point instead of paint or his instead of him), and we recorded the number of times they autocorrected by accident.Results:Bilinguals with AD translated switch words more often than controls, and ROC curves revealed good-to-excellent discrimination between patients and controls based solely on the number of errors produced during reading aloud (AUC or Area Under the Curve values ranged from .71-.92). In the second study, cognitively normal monolinguals with high CSF Tau/Aß42 (i.e., an AD-like biomarker profile) produced more autocorrect errors (e.g., saying point instead of paint)than those below the biomarker threshold, and autocorrection errors showed potential for discriminating individuals with higher AD risk from controls (AUC=.76; 95%CI .62-.90).Conclusions:Difficulty stopping automatic translation of language switch words and autocorrection during reading aloud reveals promise as a diagnostic tool. Reading aloud elicits rapid production of hundreds of words while maintaining tight experimental control over the content of speech and harnessing the power and complexity of language to enable detection of very subtle cognitive changes through simple analysis of critical targets. I will discuss the theoretical implications of this work for understanding how bilinguals choose a single language for production, the nature of cognitive impairments in early AD and areas of need for further research to maximize the potential utility of reading aloud for detection of cognitive impairment.

Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment.

Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.

Disfluencies in spontaneous speech in persons with low-grade glioma before and after surgery

ABSTRACT Impaired lexical retrieval is common in persons with low-grade glioma (LGG). Several studies have reported a discrepancy between subjective word-finding difficulties and results on formal tests. Analysis of spontaneous speech might be more sensitive to signs of word-finding difficulties, hence we aimed to explore disfluencies in a spontaneous-speech task performed by participants with presumed LGG before and after surgery. Further, we wanted to explore how the presence of disfluencies in spontaneous speech differed in the participants with and without objectively established lexical-retrieval impairment and with and without self-reported subjective experience of impaired language, speech and communication. Speech samples of 26 persons with presumed low-grade glioma were analysed with regard to disfluency features. The post-operative speech samples had a higher occurrence of fillers, implying more disfluent language production. The participants performed worse on two of the word fluency tests, and after surgery the number of participants who were assessed as having an impaired lexical retrieval had increased from 6 to 12. The number of participants who experienced a change in their language, speech or communication had increased from 9 to 12. Additional comparisons showed that those with impaired lexical retrieval had a higher proportion of false starts after surgery than those with normal lexical retrieval, and differences in articulation rate and speech rate, favouring those not having experienced any change in language, speech or communication. Taken together, the findings from this study strengthen the existing claim that temporal aspects of language and speech are important when assessing persons with gliomas.

Analysis of spontaneous speech in Parkinson's disease by natural language processing

IntroductionPatients with Parkinson's disease (PD) encounter a variety of speech-related problems, including dysarthria and language disorders. To elucidate the pathophysiological mechanisms for linguistic alteration in PD, we compared the utterance of patients and that of healthy controls (HC) using automated morphological analysis tools. MethodsWe enrolled 53 PD patients with normal cognitive function and 53 HC, and assessed their spontaneous speech using natural language processing. Machine learning algorithms were used to identify the characteristics of spontaneous conversation in each group. Thirty-seven features focused on part-of-speech and syntactic complexity were used in this analysis. A support-vector machine (SVM) model was trained with ten-fold cross-validation. ResultsPD patients were found to speak less morphemes on one sentence than the HC group. Compared to HC, the speech of PD patients had a higher rate of verbs, case particles (dispersion), and verb utterances, and a lower rate of common noun utterances, proper noun utterances, and filler utterances. Using these conversational changes, the respective discrimination rates for PD or HC were more than 80%. ConclusionsOur results demonstrate the potential of natural language processing for linguistic analysis and diagnosis of PD.

A Review of Frailty Analysis in Older Adults: From Clinical Tools Towards Fully Automated Preventive Systems

Frailty is a geriatric syndrome characterized by sarcopenia and physiological impairment. Although the majority of older adults wish to age at home, being frail threatens this choice since it increases the risk of falls and loss of functional independence. Hence, frailty screening and early detection are needed to stop or at least slow down the physical weakening process. In this paper, we present a review in which we discuss the proposed methods from the literature that targets frailty detection and analysis, starting from traditional clinical tools then introducing data-driven studies before highlighting the importance of fully automated systems. We conducted a review study by searching several databases such as Google Scholar, IEEE Xplore, MDPI, and ScienceDirect to name a few. This work presents clinical tools and classical performance tests to assess the health status and the physical function, as well as statistical and observational studies to analyze the frailty syndrome. Moreover, we discuss briefly the work of our research team in this context, represented by the development of a telemonitoring system which aims at the transition from a curative to a preventive model. Firstly, this review points out the absence of a gold standard to detect frailty in older individuals. Secondly, it discusses the limitations of self-reported measures/questionnaires and other traditional performance tests which are based on subjective data and done under supervised conditions. Thirdly, our study emphasizes the lack of robust approaches that target the early detection of frailty and the prediction of a future risk of physical worsening. We propose new research directions based, on the one hand, on automatic activity identification and tracking and, on the other hand, on the analysis of spontaneous speech of elderly. This paper describes research findings and highlights the existing gaps in the context of frailty, and serves as a state of the art for researchers. Additionally, this work suggests future research directions regarding the early detection and prevention of frailty.

Predicting Depression Severity from Spontaneous Speech as Prompted by a Virtual Agent

IntroductionOne of the major challenges in clinical psychiatry remains the absence of well established objective measures of symptoms’ severity. Clinical insights are mainly provided through keen behavioral observation and subjective questionnaires and scales.ObjectivesThe aim of this paper is to predict depression severity through speech using the features extracted from the speech as provided by participants during a semi-structured dialogue with a virtual avatar.MethodsWe use data from a subset of the DAICWOZ dataset consisting in 142 dialogues between participants and a virtual avatar during which the avatar uses several prompts to maintain a conversation with the participant. The avatar uses prompts involving the topics of travel, dream jobs, and memorable experiences. From the speech generated from the dialogue, we extract participant utterances separated by prompt and extract features from the three sets of transcripts. We extract content features from the transcript and acoustic features from the excerpt corresponding to the speech from the participant for the prompt in question.We perform regression experiments on the PHQ8 items using the features extracted from each set of transcripts. Furthermore, we combine the features extracted from each set of transcripts and compute partial spearman correlations between them and the PHQ8 items using gender as a covariate.ResultsWith our best regression model we obtain an R2 of 0.1, explaining 10% of the variance in the PHQ total score. Additionally, we obtain a mean absolute error of 1.25, suggesting that the regressor can detect with more or less precision clinically meaningful differences in depression severity between participants. Partial correlations between the total score and the features show significant correlations between features dependent on the amount of speech generated by each participant, along with the complexity of syntactic structures used.ConclusionsAutomatic analysis of spontaneous speech could help with the detection and monitoring of signs of depression. By combining the use of this technology with timely intervention strategies for instance provided by a virtual agent it could contribute to timely prevention.Disclosure of InterestA. König: None Declared, M. Mina Employee of: ki:elements GmbH, S. Schäfer Employee of: ki:elements GmbH, N. Linz Shareolder of: ki:elements GmbH, Employee of: ki:elements GmbH, J. Tröger Shareolder of: ki:elements GmbH, Employee of: ki:elements GmbH

Front vowels of Spanish: A challenge for Chinese speakers

AbstractThis research proposes to define the timbre of front vowels [e] and [i] in the spontaneous speech of the Spanish interlanguage spoken by Chinese people and to determine the convergent and divergent features of Peninsular Spanish. Variables such as gender, level of Spanish proficiency and the (a)tonicity of the vowels will also be assessed to see the extent to which these factors influence the pronunciation of the learners. A corpus of 1,489 front vowels produced by 36 Chinese speakers and a corpus of 420 vowels produced by 79 Spanish speakers were used for this study. The mean F1and F2values were calculated for each vowel. According to the statistical analysis of spontaneous speech, the interlanguage and the target language are similar in that the sounds [i] and [e] are significantly different, the atonic and tonic [i] show no significant differences and the tonic [e] is more open than the atonic [e] in both genders. However, the interlanguage diverges more from the target language because the timbre of the front vowels is more dispersed, that of [i] is more closed and fronted and that of [e] is more open and fronted, in both males and females, tonic and atonic. Finally, the study reveals that the level of language proficiency and tonicity are factors that influence the acquisition of pronunciation.

Characteristics of language and communication after subcortical lesions in the left and right hemisphere

<h3>Research Objectives</h3> The present case comparison investigates to our knowledge for the first time, whether the dichotomy of specific linguistic and communicative characteristics following left and right hemisphere cortical lesions can also be demonstrated after subcortical damage. Classically, left hemisphere cortical lesions of language-relevant areas cause aphasia (Raymer & Rothi, 2018), with cardinal symptoms on a microlinguistic level (e.g. phonologic and semantic errors). Damage to right-hemisphere cortical areas typically results in Cognitive Communication Disorders (CCDs) (Blake, 2018). Symptoms of CCDs are mainly evident on a macrolinguistic level (e.g. tangentiality, verbosity). <h3>Design</h3> Systematic single-case comparison. <h3>Setting</h3> Neurological rehabilitation clinic. <h3>Participants</h3> Two persons with comparable subcortical lesions after postacute left (Patient A) and right (Patient B) basal ganglia hemorrhage stroke. Patient A: Anomic aphasia (Huber et al., 1983), fluent spontaneous speech, short, syntactically simple sentences, word-retrieval problems. Patient B: CCD (Quinting et al., 2020), fluent spontaneous speech, complex, partially paragrammatic syntax. <h3>Interventions</h3> Participants were assessed for communicative-pragmatic ability using the ANELT (Blomert et al., 1994; Rubi-Fessen et al., 2021). Responses were analyzed with regard to linguistic and communicative-pragmatic aspects. A linguistic evaluation was performed by computer-assisted software 'Aachen spontaneous speech analysis' (ASPA, Huber et al., 2005). <h3>Main Outcome Measures</h3> A-scale of the ANELT and linguistic parameters of ASPA. <h3>Results</h3> The results of the ANELT-A-scale were comparable for both patients (Patient A: 84/100, Patient B: 81/100 points). On a microlinguistic level, Patient A showed phonematic paraphasias; ASPA analyses revealed deviations from normative data from non-brain-damaged speakers (Hussmann et al., 2012) concerning all parameters except for type-token-ratio. Patient B´s reactions were syntactically complex and less coherent, indicating problems on a macrolinguistic level. However, ASPA analyses showed results slightly below average concerning percentage words and type-token-ratio. <h3>Conclusions</h3> Typical linguistic-communicative characteristics of cortical left- and right-hemispheric lesions may appear equivalently in left- and right hemispheric subcortical damage. With comparably good communicative effectiveness in the ANELT, these characteristics can also be shown quantitatively by specific patterns in spontaneous speech parameters in ASPA analyses. <h3>Author(s) Disclosures</h3> All authors report no conflicts of interest.

Fair and Privacy-Preserving Alzheimer's Disease Diagnosis Based on Spontaneous Speech Analysis via Federated Learning.

As the most common neurodegenerative disease among older adults, Alzheimer's disease (AD) would lead to loss of memory, impaired language and judgment, gait disorders, and other cognitive deficits severe enough to interfere with daily activities and significantly diminish quality of life. Recent research has shown promising results in automatic AD diagnosis via speech, leveraging the advances of deep learning in the audio domain. However, most existing studies rely on a centralized learning framework which requires subjects' voice data to be gathered to a central server, raising severe privacy concerns. To resolve this, in this paper, we propose the first federated-learning-based approach for achieving automatic AD diagnosis via spontaneous speech analysis while ensuring the subjects' data privacy. Extensive experiments under various federated learning settings on the ADReSS challenge dataset show that the proposed model can achieve high accuracy for AD detection while achieving privacy preservation. To ensure fairness of the model performance across clients in federated settings, we further deploy fair aggregation mechanisms, particularly q-FEDAvg and q-FEDSgd, which greatly reduces the algorithmic biases due to the data heterogeneity among the clients. Clinical Relevance -The experiments were conducted on publicly available clinical datasets. No humans or animals were involved.

Language Development in Sex Chromosome Trisomies: Developmental Profiles at 2 and 4 Years of Age, and Predictive Measures

ABSTRACT Purpose Describing language development in children with sex chromosome trisomies (SCT) and testing the predictive value of early language measures on later outcomes. Method Thirteen children with SCT were followed longitudinally. Their developmental profile was assessed, with particular attention to language, at 2 and 4 years. The predictive value of direct (spontaneous speech analysis) and indirect (communicative development inventory) language measures at 2 on performances at 4 was tested. Results Language performances at both ages were lower than non-verbal development. At 2, more than 50% of the group produced less than 50 words. At 4, impaired performances were observed in speech sound development and expressive morpho-syntax. Direct measures of Pre-syntactic development predicted later global language outcomes and Sentence Repetition. The number of consonants used at 2 was significantly related to Nonword Repetition at 4. Conclusions The study highlights the importance of early detection and careful follow-up for children with SCT.