Aged to Perfection: Machine-Learning Maps of Age in Conversational English

TL;DR

This paper employs machine learning and linguistic analysis on British English speech data to identify age-related language patterns and develop models that predict speaker age groups based on linguistic features.

Contribution

It introduces a novel approach combining computational linguistics and machine learning to classify speaker age groups from spoken language data.

Findings

Identified distinctive linguistic markers for different age groups.

Developed models that accurately predict speaker age from speech features.

Enhanced understanding of sociolinguistic variation across generations.

Abstract

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.