Predicting gender and age categories in English conversations using lexical, non-lexical, and turn-taking features

Predicting gender and age categories in English conversations using lexical, non-lexical, and turn-taking features Andreas Liesenfeld, G´abor Parti, Yu-Yin Hsu, Chu-Ren Huang Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University Hong Kong amliese@polyu.edu.hk, gabor.parti@connect.polyu.hk, yu-yin.hsu@polyu.edu.hk, churen.huang@polyu.edu.hk Abstract This paper examines gender and age salience and (stereo)typicality in British English talk with the aim to predict gender and age cat- egories based on lexical, phrasal and turn- taking features. We examine the SpokenBNC, a corpus of around 11.4 million words of British English conversations and identify be- havioural differences between speakers that are labelled for gender and age categories. We explore differences in language use and turn-taking dynamics and identify a range of characteristics that set the categories apart. We find that female speakers tend to produce more and slightly longer turns, while turns by male speakers feature a higher type-token ra- tio and a distinct range of minimal particles such as “eh”, “uh” and “em”. Across age groups, we observe, for instance, that swear words and laughter characterize young speak- ers’ talk, while old speakers tend to produce more truncated words. We then use the ob- served characteristics to predict gender and age labels of speakers per conversation and per turn as a classification task, showing that non-lexical utterances such as minimal parti- cles that are usually left out of dialog data can contribute to setting the categories apart. Author’s note (Oct 2020): statement on the use of social categories in this study This work involves the labelling of participants for social categories re- lated to gender and age. We caution against the use of this heuristic due to the risk of promoting a biased view on the topics. We would like to encourage those interested in the computational modelling of social categories to join the discussion on these concerns and consider participating in efforts to build more inclusive resources for the study of the topics. 1 Introduction One of the most interesting topics in language studies has been on how speakers’ gender and age differences influence their communicative be- haviour. Transcriptions of real-world, naturally- occurring conversations provide us a window to ex- amine such differences in talk-in-interaction. Gendered and age-salient elements of talk have long been studied from a range of perspectives, in- cluding linguistics (e.g. Lakoff 1973, Tannen 1990), psychology (for an overview see, e.g. James and Drakich 1993, Tannen 1993), and conversation anal- ysis (e.g. Jefferson 1988). This topic has also been extensively studied from a computational per- spective, focusing on how the differences can be formally described and modelled. In recent years, the research interest has been extended to vari- ous applications using different types of data, such as using movie subtitles to identify gender distin- guishing features (Schofield and Mehr, 2016); email interactions to study gender and power dynamics (Prabhakaran and Rambow, 2017); video record- ings of human-robot interactions to study gendered and age-related differences in turn-taking dynamics (Skantze, 2017); literary and weblog data to study differences between male and female writing …

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