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Introduction: Automatic recognition of stutters (ARS) from speech recordings can facilitate objective assessment and intervention for people who stutter. However, the performance of ARS systems may depend on how the speech data are segmented and labelled for training and testing. This study compared two segmentation methods: event-based, which delimits speech segments by their fluency status, and interval-based, which uses fixed-length segments regardless of fluency. Methods: Machine learning models were trained and evaluated on interval-based and event-based stuttered speech corpora. The models used acoustic and linguistic features extracted from the speech signal and the transcriptions generated by a state-of-the-art automatic speech recognition system. Results: The results showed that event-based segmentation led to better ARS performance than interval-based segmentation, as measured by the area under the curve (AUC) of the receiver operating characteristic. The results suggest differences in the quality and quantity of the data because of segmentation method. The inclusion of linguistic features improved the detection of whole-word repetitions, but not other types of stutters. Discussion: The findings suggest that event-based segmentation is more suitable for ARS than interval-based segmentation, as it preserves the exact boundaries and types of stutters. The linguistic features provide useful information for separating supra-lexical disfluencies from fluent speech but may not capture the acoustic characteristics of stutters. Future work should explore more robust and diverse features, as well as larger and more representative datasets, for developing effective ARS systems.

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Local minority languages and dialects, through the local knowledge and expertise associated with them, can play major roles in analysing climate change and biodiversity loss, in facilitating community awareness of environmental crises and in setting up locally-adapted resilience and sustainability strategies. While the situation and contribution of Indigenous and Tribal Peoples are of emblematic importance, the issue of the relationships between cultural and linguistic diversity and environmental awareness and protection does not solely concern peripheral highly-specialized communities in specific ecosystems of the Global South, but constitutes a worldwide challenge, throughout all of the countries, whatever their geographical location, their economical development, or their political status. Environmental emergency and climate change resilience should therefore raise international awareness on the need to promote the survival and development of minority languages and dialects and to take into account their creativity and expertise in relation to the dynamics of their local environments.

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Languages are known to describe the world in diverse ways. Across lexicons, diversity is pervasive, appearing through phenomena such as lexical gaps and untranslatability. However, in computational resources, such as multilingual lexical databases, diversity is hardly ever represented. In this paper, we introduce a method to enrich computational lexicons with content relating to linguistic diversity. The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages. Our results, made available as browseable and downloadable computational resources, extend prior linguistics research on kinship terminology, and provide insight into the extent of diversity even within linguistically and culturally close communities.

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In the field of neurodegeneration, speech and language assessments are useful for diagnosing aphasic syndromes and for characterizing other disorders. As a complement to classic tests, scalable and low-cost digital tools can capture relevant anomalies automatically, potentially supporting the quest for globally equitable markers of brain health. However, this promise remains unfulfilled due to limited linguistic diversity in scientific works and clinical instruments. Here we argue for cross-linguistic research as a core strategy to counter this problem. First, we survey the contributions of linguistic assessments in the study of primary progressive aphasia and the three most prevalent neurodegenerative disorders worldwide-Alzheimer's disease, Parkinson's disease, and behavioural variant frontotemporal dementia. Second, we address two forms of linguistic unfairness in the literature: the neglect of most of the world's 7000 languages and the preponderance of English-speaking cohorts. Third, we review studies showing that linguistic dysfunctions in a given disorder may vary depending on the patient's language and that English speakers offer a suboptimal benchmark for other language groups. Finally, we highlight different approaches, tools and initiatives for cross-linguistic research, identifying core challenges for their deployment. Overall, we seek to inspire timely actions to counter a looming source of inequity in behavioural neurology.

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Existing studies have examined only the unidirectional impact of cultural diversity on the economic performance of countries, regions, and cities, not the other ways. They have assumed the diversity as given, while it may also grow, due to in-migration of workers and entrepreneurs, with economic growth and may be dependent on it. This paper models diversity and economic growth in a bi-directional causal frame and demonstrates that economic growth has a substantial impact on religious, language, and overall cultural diversities in the major states of India. However, the Granger causality between economic growth to language diversity and overall cultural diversity is found to be stronger and more widespread across the states than the causality from economic growth to religious diversity. The findings of this paper may have significant theoretical and empirical implications, as mainly the unidirectional way the impact of cultural diversity on economic growth has been advocated and the empirical studies have been modelled to date. Supplementary Information: The online version contains supplementary material available at 10.1007/s12115-023-00833-0.

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There is a large consensus (e.g., Cristia, Foushee, Aravena-Bravo, Cychosz, Scaff & Casillas, 2022; Kidd & Garcia, 2022) that diversification in language acquisition research is needed. Cristia et al. (2022) convincingly argue for studying language acquisition in rural populations and recommend combining observational and experimental approaches in doing so. In this commentary, we identify that diversification efforts must also include children growing up in non-western urban societies and that combining experiments with more easy-to-obtain data on language exposure can be a solid method to start with.

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In today's knowledge economy, knowledge and knowledge sharing are fundamental for organizations to achieve competitiveness and for individuals to strengthen their innovation capabilities. Knowledge sharing is a complex language-based activity; language affects how individuals communicate and relate. The growth in international collaborations and the increasing number of diverse teams affect knowledge sharing because individuals engage in daily knowledge activities in a language they are not native speakers. Understanding the challenges they face, and how they manage the emerging difficulties is the main aim of this manuscript. For this purpose, an explorative case study was conducted in an international university research project, namely the TED project. Both interviews and direct observations were employed to understand the phenomenon better and deliberately triangulate data and improve validity. Results show that non-native language use determines the emergence of different language proficiency, depending on the nature of the knowledge domain-job-related vs. non-job-related. Within non-job-related knowledge domains, the lack of linguistic abilities, summed to the perceived cultural diversities, mainly affects people's propensity to engage in personal and more intense social relationships. Under such circumstances, tacit knowledge sharing is reduced with negative consequences on the project's long-term innovative performance. Since the project is still running, detecting language challenges will allow the partners to design and apply effective measures to support cooperation with language and cultural barriers. Among them, code switching, adopted by "bridge" actors, already emerges as tool supporting communication and knowledge exchange.

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How bilinguals' experience in managing multiple languages in different communicative contexts influences the cognitive and neural aspects of executive functions remains unclear. Therefore, we examined whether variations in language experience in young adult bilinguals were associated with data-driven brain functional network patterns (connectivity and signal variability) defined by performance during executive control tasks (Stroop and task-switching). Multiple aspects of language experience, such as the extent of balanced bilingualism in language proficiency and usage, and language diversity across social contexts (i.e., language entropy) were assessed. We found that greater language diversity, rather than balanced bilingualism per se, was related to higher brain network specialization and segregation concentrated on the default mode and executive control networks, and lower signal variability, a pattern linked to smaller RT-indices related to executive functioning of shifting, goal-maintenance, and conflict-monitoring. Our findings underscore the important role of language diversity in influencing bilinguals' neurocognitive characteristics of executive functioning.

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Bilingual language representation and cognitive control effects may reflect the dynamic interactions among the complex learning environment, genotype of the individual, and developing cognitive abilities. In this paper we propose a framework considering such interactions. Specifically, we present a nonlinear, developmentally-oriented perspective in which each individual represents a developmental trajectory in multidimensional space. These trajectories focus on the cognitive ecosystem (and how said ecosystem changes over time) and individual expertise (which affects and is affected by the ecosystem). The interactions between ecosystem and expertise lead to the emergence of a system that is built to handle the communicative needs of the individual.

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This contribution theorizes the historical dynamics of so-called language isolates, languages which cannot be demonstrated to belong to any known language family. On the basis of a qualitative review of how isolates, language families or their branches lost territory to other languages through time, I develop a simple model for the genesis of isolates as a function of proximity to major geographical barriers, and pit it against an alternative view that sees them as one manifestation of linguistic diversity generally. Using a variety of statistical techniques, I test both accounts quantitatively against a worldwide dataset of language locations and distances to geographical barriers, and find support for the position that views language isolates as one manifestation of linguistic diversity generally. However, I caution that different processes which are not necessarily mutually exclusive may have shaped the present-day distribution of language isolates. These may form elements of a broader theory of language isolates in particular and language diversity in general.

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Classic linguistic theory ascribes language change and diversity to population migrations, conquests, and geographical isolation, with the assumption that human populations have equivalent language processing abilities. We hypothesize that spectral and temporal characteristics make some consonant manners vulnerable to differences in temporal precision associated with specific population allele frequencies. To test this hypothesis, we modelled association between RU1-1 alleles of DCDC2 and manner of articulation in 51 populations spanning five continents, and adjusting for geographical proximity, and genetic and linguistic relatedness. RU1-1 alleles, acting through increased expression of DCDC2, appear to increase auditory processing precision that enhances stop-consonant discrimination, favouring retention in some populations and loss by others. These findings enhance classical linguistic theories by adding a genetic dimension, which until recently, has not been considered to be a significant catalyst for language change.

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Although many hypotheses have been proposed to explain why humans speak so many languages and why languages are unevenly distributed across the globe, the factors that shape geographical patterns of cultural and linguistic diversity remain poorly understood. Prior research has tended to focus on identifying universal predictors of language diversity, without accounting for how local factors and multiple predictors interact. Here, we use a unique combination of path analysis, mechanistic simulation modelling, and geographically weighted regression to investigate the broadly described, but poorly understood, spatial pattern of language diversity in North America. We show that the ecological drivers of language diversity are not universal or entirely direct. The strongest associations imply a role for previously developed hypothesized drivers such as population density, resource diversity, and carrying capacity with group size limits. The predictive power of this web of factors varies over space from regions where our model predicts approximately 86% of the variation in diversity, to areas where less than 40% is explained.

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An evolutionary model for emergence of diversity in language is developed. We investigated the effects of two real life observations, namely, people prefer people that they communicate with well, and people interact with people that are physically close to each other. Clearly these groups are relatively small compared to the entire population. We restrict selection of the teachers from such small groups, called imitation sets, around parents. Then the child learns language from a teacher selected within the imitation set of her parent. As a result, there are subcommunities with their own languages developed. Within subcommunity comprehension is found to be high. The number of languages is related to the relative size of imitation set by a power law.

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Two types of verbal fluency tasks (letter fluency task; LFT, category fluency task; CFT) have been widely used to assess cognitive function in people with psychiatric diseases including schizophrenia. The task demand of the LFT is considered to vary across languages, as the cognitive process largely relies on sound and writing systems. Specifically, a sound unit for a letter (s) and a manner of association between them are assumed to be related with the performance. In the current study, three analyses have been conducted to examine this issue, using Japanese, Turkish, and English-speaking patients with schizophrenia. It was hypothesized that severity of letter fluency impairment would be in the order of Japanese, Turkish, and English speaking patients according to the inflexibility of a word search. First, performance on the LFT and the CFT was compared among Japanese (N=40), Turkish (N=30), and the US (N=31) patients (Analysis 1). A significant difference was found between the US and other two groups only in the LFT. Second, verbal fluency performance was compared between Japanese and Turkish patients by contrasting the degree of disassociations from normal controls (Japanese: N=20, Turkish: N=30) (Analysis 2). In Japanese patients, performance on the LFT was more severely impaired compared to that on the CFT while the opposite trend was found in the Turkish counterpart, suggesting that letter fluency performance was more degraded in Japanese patients. Finally, Analysis 3 was conducted to examine the relative order of letter fluency impairment among Japanese, Turkish and English-speaking patients. Disassociation in English users with schizophrenia was estimated based on previous meta-analytic reviews. The effect size (ES) for the letter fluency deficit was the largest in the Japanese sample, while the other two groups share similar ESs. The results from the three analyses partially supported the hypothesis for the severity of the letter fluency impairment in patients with schizophrenia. The language-dependency of letter fluency impairment was thought to be explained by the theoretical model built on unique properties of sound and writing systems. The considerations presented here would provide useful information for optimizing the portability of cognitive tasks across languages.

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There is disagreement about the routes taken by populations speaking Bantu languages as they expanded to cover much of sub-Saharan Africa. Here, we build phylogenetic trees of Bantu languages and map them onto geographical space in order to assess the likely pathway of expansion and test between dispersal scenarios. The results clearly support a scenario in which groups first moved south through the rainforest from a homeland somewhere near the Nigeria-Cameroon border. Emerging on the south side of the rainforest, one branch moved south and west. Another branch moved towards the Great Lakes, eventually giving rise to the monophyletic clade of East Bantu languages that inhabit East and Southeastern Africa. These phylogenies also reveal information about more general processes involved in the diversification of human populations into distinct ethnolinguistic groups. Our study reveals that Bantu languages show a latitudinal gradient in covering greater areas with increasing distance from the equator. Analyses suggest that this pattern reflects a true ecological relationship rather than merely being an artefact of shared history. The study shows how a phylogeographic approach can address questions relating to the specific histories of certain groups, as well as general cultural evolutionary processes.

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