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

arXiv: https://arxiv.org/abs/2102.13355 · HTML: https://mlsanity.com/paper/2102.13355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Personalization in Goal-Oriented Dialog](https://mlsanity.com/paper/1706.07503/) (2017)
- [Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ](https://mlsanity.com/paper/1703.00565/) (2017)
- [Dialog Structure Through the Lens of Gender, Gender Environment, and Power](https://mlsanity.com/paper/1706.03441/) (2017)

## Built on by (most cited first, 2 of 2)

- [Prompt-Guided Turn-Taking Prediction](https://mlsanity.com/paper/2506.21191/) (2025)
- [Cross-Language Speaker Attribute Prediction Using MIL and RL](https://mlsanity.com/paper/2601.04257/) (2026)

## Abstract (arXiv)

This paper examines gender and age salience and (stereo)typicality in British English talk with the aim to predict gender and age categories 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 behavioural 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 ratio 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 speakers' talk, while old speakers tend to produce more truncated words. We then use the observed 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 particles that are usually left out of dialog data can contribute to setting the categories apart.