Preprocessing Large-Scale Conversational Datasets: A Framework and Its Application to Behavioral Health Transcripts.

ABSTRACT:

Background

The rise of artificial intelligence and accessible audio equipment has led to a proliferation of recorded conversation transcripts datasets across various fields. However, automatic mass recording and transcription often produce noisy, unstructured data that contain unintended recordings such as hallway conversations, media (eg, TV, radio), or transcription inaccuracies as speaker misattribution or misidentified words. As a result, large conversational transcript datasets require careful preprocessing and filtering to ensure their research utility. This challenge is particularly relevant in behavioral health contexts (eg, therapy, counseling) where deriving meaningful insights, specifically dynamic processes, depends on accurate conversation representation.

Objective<

SUBMITTER: Naim PM 

PROVIDER: S-EPMC12551936 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature