Abstract
Body-worn camera (BWC) audio presents unique challenges including high ambient noise, variable recording conditions, and multiple overlapping speakers that make automated transcription and speaker labeling challenging. We propose a dual-path conversational intelligence framework that preprocesses raw BWC audio, separates the processing pipeline into a diarization branch and an ASR branch, and fuses their outputs. The diarization branch uses a denoising front-end (DeepFilterNet), voice activity detection (VAD), and NVIDIA's Multi-Scale Speaker Diarization Decoder (MSDD) with TitaNet embeddings. The transcription branch uses loudness normalization and WhisperX (Large-v3) with forced alignment and probability-guided speech segmentation. Finally, word-level speaker attribution is performed by assigning each recognized word to the speaker segment with the greatest temporal overlap. We evaluate the proposed framework on a curated body-worn camera dataset constructed from publicly available U.S. and U.K. police body-worn camera recordings. Experimental results demonstrate that task-specific acoustic conditioning and probability-guided speech segmentation improve speaker diarization, transcription, and word-level speaker attribution under challenging body-worn camera recording conditions. The proposed modular architecture provides an extensible foundation for future speaker-aware conversational intelligence systems.
Abstract (translated)
URL
https://arxiv.org/abs/2607.24958
