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.