As the popularity and adoption of Artificial Intelligence (AI) systems continue to rise, this article presents a promising proposition: the use of AI dialects to enhance AI perception. By delving into the potential of personalized AI dialects to augment user perceptions of warmth, competence, and authenticity, the article underscores the pivotal role of anthropomorphism in fortifying trust, satisfaction, and loyalty to AI systems. A comprehensive research framework is put forth to explore these potential mechanisms and outcomes of AI dialect introduction, shedding light on how these impacts might vary based on AI modality (text, voice, and video), industry adoption, and user demographics.

We studied social-psychological effects over time of a faculty-mentor workshop intervention that addressed attitudes associated with language variety and their impact on scientific communication (SC) skill development of PhD and postdoctoral STEM research trainees (N = 274). Six months after their mentors attended the workshop, all mentees had significant gains in productivity in speaking tasks. In particular, mentees with high language discomfort rated their quality of communication with their mentor and their enthusiasm about communicating more highly (p < .05 for both measures), compared to mentees with low language discomfort. In addition, mentees raised speaking nonstandardized varieties of English reported significant reductions in discomfort related to language use (p = .003), compared to mentees raised speaking standardized English. We conclude that training mentors to understand and respond to language diversity and development results in multiple beneficial outcomes for mentees, including the amelioration of language-variety related discomfort in the research environment.

Background: Robots are increasingly used as interaction partners with humans. Social robots are designed to follow expected behavioral norms when engaging with humans and are available with different voices and even accents. Some studies suggest that people prefer robots to speak in the user\'s dialect, while others indicate a preference for different dialects. Methods: Our study examined the impact of the Berlin dialect on perceived trustworthiness and competence of a robot. One hundred and twenty German native speakers (M age = 32 years, SD = 12 years) watched an online video featuring a NAO robot speaking either in the Berlin dialect or standard German and assessed its trustworthiness and competence. Results: We found a positive relationship between participants\' self-reported Berlin dialect proficiency and trustworthiness in the dialect-speaking robot. Only when controlled for demographic factors, there was a positive association between participants\' dialect proficiency, dialect performance and their assessment of robot\'s competence for the standard German-speaking robot. Participants\' age, gender, length of residency in Berlin, and device used to respond also influenced assessments. Finally, the robot\'s competence positively predicted its trustworthiness. Discussion: Our results inform the design of social robots and emphasize the importance of device control in online experiments.

The current study explored the intelligibility and acceptability ratings of dysarthric speakers with African American English (AAE) and General American English (GAE) dialects by listeners who identify as GAE or AAE speakers, as well as listener ability to identify dialect in dysarthric speech. Eighty-six listeners rated the intelligibility and acceptability of sentences extracted from a passage read by speakers with dysarthria. Samples were used from the Atlanta Motor Speech Disorders Corpus and ratings were collected via self-report. The listeners identified speaker dialect in a forced-choice format. Listeners self-reported their dialect and exposure to AAE. AAE dialect was accurately identified in 63.43% of the the opportunities; GAE dialect was accurately identified in 70.35% of the opportunities. Listeners identifying as AAE speakers rated GAE speech as more acceptable, whereas, listeners identifying as GAE speakers rated AAE speech as more acceptable. Neither group of listeners demonstrated a difference in intelligibility ratings. Exposure to AAE had no effect on intelligibility or acceptability ratings. Listeners can identify dialect (AAE and GAE) with a better than chance degree of accuracy. One\'s dialect may have an effect on intelligibility and acceptability ratings. Exposure to a dialect did not affect listener ratings of intelligibility or acceptability.

BACKGROUND: The dialect spoken by children influences diagnostic decision-making regarding the identification and severity of speech sound disorder (SSD).
OBJECTIVE: The primary objective was to review papers that examined the influence of dialect on the identification of SSD in Vietnamese-speaking children.
METHODS: Five studies of mono- and multilingual Vietnamese-speaking children living in Vietnam and Australia were reviewed to examine the influence of dialect on the assessment and analysis children\'s speech. The main Vietnamese dialects (Standard, Northern, Central, Southern) differ in the production of consonants, vowels and tones.
RESULTS: Most speech assessments define correct production using the standard dialect of a language. Insights from recent studies of Vietnamese provide recommendations for also considering dialect in diagnostic decision-making. First, we recommend adding column(s) to the assessment score sheet that includes the dialectal variants spoken by adults in the child\'s family or community. Second, we calculate the accuracy of production twice, based on the standard form and dialectal form. Third, we report the percentage of consonants correct-standard (PCC-S) and percentage of consonants correct-dialect (PCC-D).
CONCLUSIONS: Diagnostic decision-making is influenced by dialectal variation in children\'s speech, so speech and language therapists need to compare standard and dialectal productions when undertaking assessments, analysis and diagnostic decision-making.
CONCLUSIONS: What is already known on the subject Most speech assessments use the standard form of a dialect as the correct production. The standard dialect is used for diagnosis of SSD and identification of intervention targets. What this paper adds to the existing knowledge This paper examines five research studies of Vietnamese to identify ways to consider dialect in speech and language therapy assessment and analysis. Vietnamese provides a complex environment for this examination since there are numerous Vietnamese dialects and they differ according to consonants, vowels and tones. What are the potential or actual clinical implications of this work? Speech and language therapists are encouraged to add column(s) to their assessment score sheet that includes the dialectal variants spoken by adults and to report the PCC-S and PCC-D.

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.

In background noise, the amplitude fluctuations of speech commonly provide for momentary glimpses of high intensity portions of speech, predominantly from vowels. Previous investigations have provided glimpses of consonants or vowels to determine the perceptual contribution of different speech acoustics to sentence intelligibility. The present study investigated the consistency of perceptual contributions across eight American English dialects for a group of listeners from the southern United States. Results demonstrated that sentences preserving predominant vowel acoustics were consistently more intelligible across dialects for all participants. The significant contribution of vowels does not appear dependent on familiarity with properties of dialectal variation but may represent the preservation of more general acoustic cues important for sentence recognition. Acoustic analyses of temporal amplitude modulation suggest important cues present during vowels and highlight gradient differences across dialects associated with intelligibility.

The goal of the current study was to examine the role of variety and frequency of dialectal features in relation to General American English (GAE) editing ability. This study focused on speakers of dialect-specific forms and their ability to edit to GAE. To gain insight into this relation, the following research questions were posed: (1). How do third and fifth-grade students differ in the variety of features of dialect-specific forms they use, the frequency of their use, and their editing ability to GAE? (2). Is there a significant relation between dialect use and editing ability?
Participants included 68 third and fifth-grade students who produced at least one dialect-specific form. We measured students\' ability to edit to GAE during a written editing task. Frequency and variety of dialect use were based on an oral language sample where students were asked to share their favorite game or sport.
Fifth-grade students are significantly better at editing written dialect specific forms to reflect GAE writing conventions as compared to third-grade students. However, there was not a significant difference in the dialect specific form usage between the two grades. Finally, there was not a significant relation between dialect specific form use and editing ability.
These results offer relevant clinical and educational implications for increasing cultural responsivity and promote the use of multiple measures across modalities to gain relevant information when assessing students who use dialect specific forms. Further, the results from this study provide further insights into how written editing ability exhibited through the awareness of GAE conventions improves with age despite the influence of dialect specific forms.

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Citizen Science (CS) is a research approach that has become popular in recent years and offers innovative potential for dialect research in ornithology. As the scepticism about CS data is still widespread, we analysed the development of a 3-year CS project based on the song of the Common Nightingale (Luscinia megarhynchos) to share best practices and lessons learned. We focused on the data scope, individual engagement, spatial distribution and species misidentifications from recordings generated before (2018, 2019) and during the COVID-19 outbreak (2020) with a smartphone using the \'Naturblick\' app. The number of nightingale song recordings and individual engagement increased steadily and peaked in the season during the pandemic. 13,991 nightingale song recordings were generated by anonymous (64%) and non-anonymous participants (36%). As the project developed, the spatial distribution of recordings expanded (from Berlin based to nationwide). The rates of species misidentifications were low, decreased in the course of the project (10-1%) and were mainly affected by vocal similarities with other bird species. This study further showed that community engagement and data quality were not directly affected by dissemination activities, but that the former was influenced by external factors and the latter benefited from the app. We conclude that CS projects using smartphone apps with an integrated pattern recognition algorithm are well suited to support bioacoustic research in ornithology. Based on our findings, we recommend setting up CS projects over the long term to build an engaged community which generates high data quality for robust scientific conclusions.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s10336-022-02018-8.
Gesellschaftliches Engagement und Datenqualität: Bewährte Praktiken und Erfahrungen aus einem bürgerwissenschaftlichen Projekt zum Vogelgesang Citizen Science (CS) ist eine Forschungsmethode, die in den letzten Jahren an Bedeutung gewonnen hat und innovatives Potenzial für die Dialektforschung in der Ornithologie bietet. Da die Vorbehalte gegenüber CS-Daten immer noch weit verbreitet sind, haben wir die Entwicklung eines dreijährigen CS-Projekts zum Gesang der Nachtigall (Luscinia megarhynchos) analysiert, um bewährte Praktiken und gewonnene Erfahrungen darzustellen. Wir fokussierten uns auf den Datenumfang, das individuelle Engagement von Teilnehmenden, die räumliche Verteilung und die Fehlbestimmungen von Arten aus Aufnahmen, die vor (2018, 2019) und während des COVID-19-Ausbruchs (2020) mit einem Smartphone unter Verwendung der \"Naturblick\" App erstellt wurden. Die Anzahl der Aufnahmen von Nachtigallgesängen und das individuelle Engagement stiegen stetig an und erreichten ihren Höhepunkt in der Saison während der Pandemie. 13.991 Aufnahmen von Nachtigallgesängen wurden von anonymen (64%) und nicht-anonymen Teilnehmenden (36%) erstellt. Im Laufe des Projekts weitete sich die räumliche Verteilung der Aufnahmen aus (von Berlin auf bundesweit). Die Rate der Fehlbestimmungen war gering, ging im Laufe des Projekts zurück (von 10% auf 1%) und wurde hauptsächlich von gesanglichen Ähnlichkeiten mit anderen Vogelarten beeinflusst. Unsere Studie zeigte außerdem, dass das gesellschaftliche Engagement und die Datenqualität nicht direkt von den durchgeführten Disseminationsaktivitäten beeinflusst wurden, sondern dass erstere von externen Faktoren abhingen und letztere von der App profitierte. Wir schließen daraus, dass CS-Projekte, die Smartphone-Apps mit einem integrierten Mustererkennungsalgorithmus verwenden, gut geeignet sind, um die bioakustische Forschung in der Ornithologie zu unterstützen. Auf der Grundlage unserer Ergebnisse empfehlen wir, CS-Projekte langfristig zu etablieren, um eine aktive Teilnehmergemeinschaft (Community) aufzubauen, die qualitativ hochwertige Daten für fundierte wissenschaftliche Schlussfolgerungen generiert.