Aged to Perfection: Machine-Learning Maps of Age in Conversational English
This work addresses sociolinguistic diversity in modern British speech, but it is incremental as it applies existing methods to new data without broad SOTA impact.
The study tackled the problem of mapping language patterns to age groups in conversational English, using the British National Corpus 2014 to analyze factors like utterance duration and lexical diversity, and developed machine learning models to predict speaker age groups.
The study uses the British National Corpus 2014, a large sample of contemporary spoken British English, to investigate language patterns across different age groups. Our research attempts to explore how language patterns vary between different age groups, exploring the connection between speaker demographics and linguistic factors such as utterance duration, lexical diversity, and word choice. By merging computational language analysis and machine learning methodologies, we attempt to uncover distinctive linguistic markers characteristic of multiple generations and create prediction models that can consistently estimate the speaker's age group from various aspects. This work contributes to our knowledge of sociolinguistic diversity throughout the life of modern British speech.