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

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: We present a framework for preprocessing large datasets of conversational transcripts and filtering out non-sessions—transcripts that do not reflect a behavioral treatment session but instead capture unrelated conversations or background noise. This framework is applied to a large dataset of behavioral health transcripts from community mental health clinics across the United States. Methods: Our approach integrated basic feature extraction, human annotation, and advanced applications of large language models (LLMs). We began by mapping transcription errors and assessing the number of non-sessions. Next, we extracted statistical and structural features to characterize transcripts and detect outliers. Notably, we used LLM perplexity as a measure of comprehensibility to assess transcript noise levels. Finally, we used zero-shot prompting with an LLM to classify transcripts as sessions or non-sessions, validating its output against expert annotations. Throughout, we prioritized data security by selecting tools that preserve anonymity and minimize the risk of data breaches. Results: Initial assessment revealed that transcription errors—such as incomprehensible segments, unusually short transcripts, and speaker diarization issues—were present in approximately one-third (n=36 out of 100) of a manually reviewed sample. Statistical outliers revealed that high speaking rate (>3.5 words per second) is associated with short transcripts and answering machine messages, while short conversation duration (<15 min) was an indicator for case management sessions. The 75th percentile of LLM perplexity scores was significantly higher in non-sessions than sessions (permutation test mean difference = −258, P =.02), although this feature alone offered only moderate classification performance (precision =0.63, recall =0.23 after outlier removal). In contrast, zero-shot LLM prompting effectively distinguished sessions from non-sessions with high agreement to expert ratings (κ=0.71) while also capturing the nature of the meeting. Conclusions: This study’s hybrid approach effectively characterizes errors, evaluates content, and distinguishes text types within unstructured conversational dataset. It provides a foundation for research on conversational data, key methods, and practical guidelines that serve as crucial first steps in ensuring data quality and usability, particularly in the context of mental health sessions. We highlight the importance of integrating clinical experts with artificial intelligence tools while prioritizing data security throughout the process.