People with schizophrenia spectrum disorder (SSD)—a psychiatric disorder, and people with Wernicke’s aphasia — an acquired neurological disorder, are both known to display semantic deficits in their spontaneous speech outputs. Very few studies directly compared the two groups on their spontaneous speech (Gerson et al., 1977; Faber et al., 1983), and no consistent results were found. Our study uses word (based on the word2vec model with moving windows across words) and sentence (transformer based-model) embeddings as features for a machine learning classification model to differentiate between the spontaneous speech of both groups. Additionally, this study uses these measures to differentiate between people with Wernicke’s aphasia and healthy controls. The model is able to classify patients with Wernicke’s aphasia and patients with SSD with a cross-validated accuracy of 81%. Additionally, it is also able to classify patients with Wernicke’s aphasia versus healthy controls and SSD versus healthy controls with cross-validated accuracy of 93.72% and 84.36%, respectively. For the SSD individuals, sentence and/or discourse level features are deemed more informative by the model, whereas for the Wernicke group, only intra-sentential features are more informative. Overall, we show that NLP-based semantic measures are sensitive to identifying Wernicke’s aphasic and schizophrenic speech.

The study employs a semi-automatic approach to analyze speech rate in spoken Italian, aiming to identify acoustic parameters associated with perceptual atypicality in the speech of children diagnosed with Autism Spectrum Disorder (ASD). The research focuses on a dataset comprising recordings of semi-spontaneous interactions, in comparison with interviews of Typically Developing (TD) children. A detailed examination of speech rate variability is conducted, progressing from assessing overall speech rate in conversation to the analysis of individual utterances. Furthermore, salient syllables within utterances are identified using an automatic procedure through the Salient Detector Praat script and analyzed for stress position. The study highlights specific speech style, including rapid-telegraphic and reading-performed speech. Additionally, it reveals a higher speech rate with the increasing length of utterance when <10 syllables; conversely, a speech rate diminishing in 20-25 syllables utterances, suggesting potential difficulty in producing longer utterances associated with increased cognitive load.

Speech analysis is gaining significance for monitoring neurodegenerative disorders, but with a view of application in clinical practice, solid evidence of the association of language features with cognitive scores is still needed. A cross-linguistic investigation has been pursued to examine whether language features show significance correlation with two cognitive scores, i.e. Mini-Mental State Examination and ki:e SB-C scores, on Alzheimer’s Disease patients. We explore 23 language features, representative of syntactic complexity and semantic richness, extracted on a dataset of free speech recordings of 138 participants distributed in four languages (Spanish, Catalan, German, Dutch). Data was analyzed using the speech library SIGMA; Pearson’s correlation was computed with Bonferroni correction, and a mixed effects linear regression analysis is done on the significant correlated results. MMSE and the SB-C are found to be correlated with no significant differences across languages. Three features were found to be significantly correlated with the SB-C scores. Among these, two features of lexical richness show consistent patterns across languages, while determiner rate showed language-specific patterns.

In the last decade, a rapidly growing body of studies has shown promising results for the automatic detection and extraction of speech and language features as biomarkers of neurodegenerative conditions such as Alzheimer’s disease. This has sparked great optimism and the development of various digital health tools, but also warnings regarding the predominance of English in the field and calls for linguistically diverse research as well as global, equitable access to novel clinical instruments. To automatically extract clinically relevant features from transcripts in low-resource languages, two approaches are possible: 1) utilizing a limited range of language-specific tools or 2) translating text to English and then extracting the features. We evaluate these approaches for part-of-speech (POS) rates in transcripts of recorded picture descriptions from a cross-sectional study of Icelandic speakers at different stages of Alzheimer’s disease and healthy controls. While the translation method merits further exploration, only a subset of the POS categories show a promising correspondence to the direct extraction from the Icelandic transcripts in our results, indicating that the translation method has to be linguistically validated at the individual POS category level.

Linguistic alterations represent one of the prodromal signs of cognitive decline associated with Dementia. In recent years, a growing body of work has been devoted to the development of algorithms for the automatic linguistic analysis of both oral and written texts, for diagnostic purposes. The extraction of Digital Linguistic Biomarkers from patients’ verbal productions can indeed provide a rapid, ecological, and cost-effective system for large-scale screening of the pathology. This article contributes to the ongoing research in the field by exploring a traditionally less studied aspect of language in Dementia, namely the rhythmic characteristics of speech. In particular, the paper focuses on the automatic detection of rhythmic features in Italian-connected speech. A landmark-based system was developed and evaluated to segment the speech flow into vocalic and consonantal intervals and to calculate several rhythmic metrics. Additionally, the reliability of these metrics in identifying Mild Cognitive Impairment and Dementia patients was tested.

Language assessment plays a crucial role in diagnosing and treating individuals with speech, language, and communication disorders caused by neurogenic conditions, whether developmental or acquired. To support clinical assessment and research, we developed Open Brain AI (https://openbrainai.com). This computational platform employs AI techniques, namely machine learning, natural language processing, large language models, and automatic speech-to-text transcription, to automatically analyze multilingual spoken and written productions. This paper discusses the development of Open Brain AI, the AI language processing modules, and the linguistic measurements of discourse macro-structure and micro-structure. The fast and automatic analysis of language alleviates the burden on clinicians, enabling them to streamline their workflow and allocate more time and resources to direct patient care. Open Brain AI is freely accessible, empowering clinicians to conduct critical data analyses and give more attention and resources to other critical aspects of therapy and treatment.

Much work has gone into developing language models of increasing size, but only recently have we begun to examine them for pernicious behaviour that could lead to harming marginalised groups. Following Lin et al. (2022) in rooting our work in psychological research, we prompt two masked language models (MLMs) of different specialisations in English and Spanish with statements from a questionnaire developed to measure stigma to determine if they treat physical and mental illnesses equally. In both models we find a statistically significant difference in the treatment of physical and mental illnesses across most if not all latent constructs as measured by the questionnaire, and thus they are more likely to associate mental illnesses with stigma. We then examine their training data or data retrieved from the same domain using a computational implementation of the Stereotype Content Model (SCM) (Fiske et al., 2002; Fraser et al., 2021) to interpret the questionnaire results based on the SCM values as reflected in the data. We observe that model behaviour can largely be explained by the distribution of the mentions of illnesses according to their SCM values.

This paper presents a methodological approach for establishing control corpora in the context of depression detection in the Modern Greek language. We discuss various methods used to create control corpora, focusing on the challenge of selecting representative samples from the general population when the target reference is the depressed population. Our approach includes traditional random selection among Twitter users, as well as an innovative method for creating topic-oriented control corpora. Through this study, we provide insights into the development of control corpora, offering valuable considerations for researchers working on similar projects in linguistic analysis and mental health studies. In addition, we identify several dominant topics in the depressed population such as religion, sentiments, health and digestion, which seem to align with findings consistently reported in the literature

This paper evaluates global and local semantic coherence in aphasic and non-aphasic discourse tasks using the Tool for the Automatic Analysis of Cohesion (TAACO). The motivation for this paper stems from a lack of automatic methods to evaluate discourse-level phenomena, such as semantic cohesion, in transcripts derived from persons with aphasia. It leverages existing test-retest data to evaluate two main objectives: (1) Test-Retest Reliability, to identify if variables significantly differ across test and retest time points for either group (aphasia, control), and (2) Inter-Group Discourse Cohesion, where aphasic discourse is expected to be less cohesive than control discourse, resulting in lower cohesion scores for the aphasia group. Exploratory analysis examines correlations between variables for both groups, identifying any relationships between word-level and sentence-level semantic variables. Results verify that semantic cohesion and coherence are generally preserved in both groups, except for word-level and a few sentence-level semantic measures,w which are higher for the control group. Overall, variables tend to be reliable across time points for both groups. Notably, the aphasia group demonstrates more variability in cohesion than the control group, which is to be expected after brain injury. A close relationship between word-level indices and other indices is observed, suggesting a disconnection between word-level factors and sentence-level metrics.

This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.

In this study, we rigorously evaluated eight machine learning and deep learning classifiers for identifying Alzheimer’s Disease (AD) patients using crosslinguistic acoustic features automatically extracted from one-minute oral picture descriptions produced by speakers of American English, Korean, and Mandarin Chinese. We employed eGeMAPSv2 and ComParE feature sets on segmented and non-segmented audio data. The Multilayer Perceptron model showed the highest performance, achieving an accuracy of 83.54% and an AUC of 0.8 on the ComParE features extracted from non-segmented picture description data. Our findings suggest that classifiers trained with acoustic features extracted from one-minute picture description data in multiple languages are highly promising as a quick, language-universal, large-scale, remote screening tool for AD. However, the dataset included predominantly English-speaking participants, indicating the need for more balanced multilingual datasets in future research.