The influence of ethnicity and language variation on undergraduates' evaluations of Dutch-speaking instructors in Belgium: A contextualized speaker evaluation experiment

Lybaert, Chloé; Hoof, Sarah Van; Deygers, Bart

doi:10.1016/j.langcom.2022.01.004

Cited by 3 publications

(3 citation statements)

References 63 publications

(91 reference statements)

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“…A chatbot such as ChatGPT (Chat Generative Pre-Trained Transformer) is a software application (generally on-line) that typically utilises GenAI and an LLM. When considering chatbots in e-commerce, there are two approaches to interactions: (a) using formal language and (b) using informal (possibly colloquial language which is generally nationality and ethnically specific [36]) language used in ordinary or familiar conversation [37].…”

Section: Large Language Models and Chatbotsmentioning

confidence: 99%

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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ChatGPT plays significant roles in the third decade of the 21st Century. Smart cities applications can be integrated with ChatGPT in various fields. This research proposes an approach for developing large language models using generative artificial intelligence models suitable for small- and medium-sized enterprises with limited hardware resources. There are many generative AI systems in operation and in development. However, the technological, human, and financial resources required to develop generative AI systems are impractical for small- and medium-sized enterprises. In this study, we present a proposed approach to reduce training time and computational cost that is designed to automate question–response interactions for specific domains in smart cities. The proposed model utilises the BLOOM approach as its backbone for using generative AI to maximum the effectiveness of small- and medium-sized enterprises. We have conducted a set of experiments on several datasets associated with specific domains to validate the effectiveness of the proposed model. Experiments using datasets for the English and Vietnamese languages have been combined with model training using low-rank adaptation to reduce training time and computational cost. In comparative experimental testing, the proposed model outperformed the `Phoenix’ multilingual chatbot model by achieving a 92% performance compared to `ChatGPT’ for the English benchmark.

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Section: Large Language Models and Chatbotsmentioning

confidence: 99%

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Tuan,

Moore,

Thanh

et al. 2024

Applied Sciences

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“…Indications that the use of standard vs. colloquial language by ethnic minority speakers can lead to different perceptions are provided by Lybaert et al (2022), who investigated the influence of ethnicity (a Flemish vs. Moroccan name), a marker of religious affiliation (wearing a hijab vs. not wearing one) and language variety (Standard vs. Colloquial Dutch) on students' evaluations of a female university teacher, but did not include accent as a marker of ethnicity (although the speaker used a mild Antwerp accent to enhance the naturalness of the speech samples). The authors found that university students evaluated the hijab-wearing teacher with a Moroccan background who spoke the standard variety more positively than her Colloquial Dutch speaking counterpart and all other guises (without hijab and with a Flemish or a Moroccan name).…”

Section: Introductionmentioning

confidence: 99%

“…The authors found that university students evaluated the hijab-wearing teacher with a Moroccan background who spoke the standard variety more positively than her Colloquial Dutch speaking counterpart and all other guises (without hijab and with a Flemish or a Moroccan name). Lybaert et al hypothesised that the veiled teacher's proficiency in Standard Dutch "surprised" the students in that they possibly held lower expectations with regard to her language use, which may have led them to better appreciate her efforts to use the standard (Lybaert et al, 2022). These results suggest that the use of Standard vs. Colloquial Dutch does influence attitudes towards ethnic minority members, at least in the context of higher education and thus for highly educated speakers (university lecturers).…”

Section: Introductionmentioning

confidence: 99%

Examining accent bias towards Turkish speakers of Dutch

Van Puyvelde,

Van Hoof,

Lybaert

et al. 2023

DuJAL

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This contribution investigates the attitudes of Flemish first language speakers towards Turkish-Flemish speakers of Dutch as a second language. We conducted a 2 x 2 x 2 speaker evaluation experiment measuring the effects of accent (native vs. Turkish), language variety (standard vs. colloquial) and name (Flemish vs. Turkish) on attitudes vis-à-vis male speakers of Belgian Dutch. Our findings provide no consistent evidence of a negative bias vis-à-vis Turkish names in Flanders. While this result could be attributed to a social desirability bias, consistent downgrading of the Turkish accent on Superiority provides an indication of the existence of an accent bias that penalises ethnic minority accents in competence-related judgements.

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Visual Cues to Speakers’ Religious Affiliation and Listeners’ Understanding of Second Language French Speech

Kennedy,

Trofimovich,

Lindberg

et al. 2024

Languages

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Previous research has shown that speakers’ visual appearance influences listeners’ perception of second language (L2) speech. In Québec, Canada, the context of this study, pandemic mask mandates and a provincial secularism law elicited strong societal reactions. We therefore examined how images of speakers wearing religious and nonreligious coverings such as medical masks and headscarves influenced the comprehensibility (listeners’ ease of understanding) and intelligibility of L2 French speech. Four L2 French women from first language (L1) Arabic backgrounds wore surgical masks while recording 40 sentences from a standardized French-language speech perception test. A total of 104 L1 French listeners transcribed and rated the comprehensibility of the sentences, paired with images of women in four visual conditions: uncovered face, medical mask, hijab (headscarf), and niqab (religious face covering). Listeners also completed a questionnaire on attitudes toward immigrants, cultural values, and secularism. Although intelligibility was high, sentences in the medical mask condition were significantly more intelligible and more comprehensible than those in the niqab condition. Several attitudinal measures showed weak correlations with intelligibility or comprehensibility in several visual conditions. The results suggest that listeners’ understanding of L2 sentences was negatively affected by images showing speakers’ religious affiliation, but more extensive follow-up studies are recommended.

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The influence of ethnicity and language variation on undergraduates' evaluations of Dutch-speaking instructors in Belgium: A contextualized speaker evaluation experiment

Cited by 3 publications

References 63 publications

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

A Generative Artificial Intelligence Using Multilingual Large Language Models for ChatGPT Applications

Examining accent bias towards Turkish speakers of Dutch

Visual Cues to Speakers’ Religious Affiliation and Listeners’ Understanding of Second Language French Speech

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