  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.
