A quantitative analysis of age-related differences in Hindi–English code-switching

Abstract

Aims and objectives: Numerical indices developed by Guzman et al. that helped characterize code-switching (CS) patterns in Spanish–English bilingual corpora, were tested on a Hindi–English bilingual corpus. Two main research questions were addressed: first, how does Hindi–English compare with Spanish–English, and second, are there measurable differences in broad CS patterns between older and younger speakers? Methodology: Television interviews of Hindi movie (Bollywood) personalities were transcribed and coded for Hindi and English lexemes. Bespoke software in Python was used to calculate the required indices, which provided information on variables such as the level of language mixing, switching probability, and the distributions of single-language spans. Further indices, such as mean span length and an approximate ratio of insertions to alternations were also calculated. Data and analysis: The indices calculated for the Hindi–English corpus broadly match those calculated for Spanish–English. Statistically significant differences between the older and younger group were detected for some key indices, with older speakers generally using less English. High levels of intra-group variability may be responsible for some indices not showing statistically significant diachronic change. Conclusions: The Guzman et al. indices suggest that Hindi–English and Spanish–English CS resemble each other in certain ways. There have been broad changes in Hindi–English CS patterns over the last few decades, but there are indications that the CS behaviour of individual speakers might change in different ways. Originality: This is the first study to systematically and quantitatively investigate age-related differences in Hindi–English CS, using naturalistic speech in semi-controlled conditions. Implications: The quantitative indices investigated in this study can be used to compare CS behaviour in different language pairs, and can also help detect diachronic changes in CS patterns.

Inter-individual differences in Hindi–English code-switching: A quantitative approach

“…A bilingual corpus of approximately 140,000 words (approximately 11 hours 20 minutes of video in total), based on TV interviews of Hindi movie (Bollywood) personalities was analysed (see Elliot and Si [2021] for more details). All of the videos (except two) comprised of interviews led by prominent film critic and industry analyst Komal Nahta ( KN ) for the TV show ETC Bollywood Business; these can be found on the programme’s YouTube channel: https://www.youtube.com/user/ETCNetworks/.…”

“…In addition, two further interviews of two guests ( SRK and AK ) from the ‘older’ category, but recorded in the 1990s with different interviewers, were transcribed and coded similarly. While age-related differences are not a focus of this study, the labels ‘older’ and ‘younger’ were maintained for the sake of convenience, as this paper immediately follows an earlier publication by Elliot and Si (2021), where the two age groups were compared explicitly. Moreover, it was of some interest to determine whether the clusters generated by the current analysis would in any way reflect the age categories.…”

“…Two of the six indices presented in Guzman et al (2017), namely I-Index (which denotes probability of switching) and Burstiness (which gives an indication of the regularity of switching), were calculated with bespoke software in Python for each interview transcript. As mentioned in Elliot and Si (2021), these indices provide important insights into the arrangement of tokens of both languages in an utterance. Moreover, our analysis scans the entirety of each individual’s speech output, instead of focusing on particular extracts.…”

“…Moreover, the analyses focused on differences between four pre-determined participant age groups. The current study draws on a much larger language corpus (albeit with a similar number of speakers to the Weston, 2012, study), and uses five different numerical indices, developed by Guzman et al (2017) and Elliot and Si (2021). Importantly, while basic comparisons are made between ‘older’ and ‘younger’ speakers, the final cluster analysis concentrates on the numerical indices only, in an attempt to find broad patterns in the CS strategy of individual speakers, and to group together similar speakers based on the indices.…”

Bridging the Gaps between Knowledge and Needs: Sociolinguistics Materials Based on Project-Based Learning

“…As implied in the definition, Sociolinguistics study is extensive because the use of language in society can include the use of language in a different community (urban community, rural community, government offices, and others), sectors (economy, education, politics, art, film, and others), and professions (farmers, fishermen, and others). Considering this vast range of scopes, the researcher will limit the Sociolinguistics study topics in this research on 11 topics, namely 1) varieties of language (Alhamami, 2020;Clements, 2018;Gelek, 2017;Yule, 2006;Tamargo et al, 2019;Hornberger & McKay, 2010;Khizhnyak & Annenkova, 2021;Murchadha & Flynn, 2018;Subhan, 2004;Vari & Tamburelli, 2020;Wardhaugh & Fuller, 2015); 2) dialects, sociolects, idiolects, and registers (Subhan, 2004;Wardhaugh & Fuller, 2015), 3) standard and non-standard varieties (Subhan, 2004;Wardhaugh & Fuller, 2015), 4) varieties of English (Bruyèl-Olmedo & Juan-Garau, 2020;Heller et al, 2017;Lee, 2022;Ozyumenko, 2020;Proshina & Nelson, 2020;Subhan, 2004), 5) code-switching (Ellison & Si, 2021;Tamargo et al, 2019;Liu, 2021;Muthusamy et al, 2020;Subhan, 2004), 6) codemixing (Ramzan et al, 2021;Subhan, 2004;Tarihoran et al, 2022;Tramutoli, 2021), 7) bilingualism, multilingualism, and diglossia (Subhan, 2004;Wardhaugh & Fuller, 2015), 8) verbal and non-verbal communication (Subhan, 2004;…”