What the Waveform Knows: Transparent-first Speech and Audio Intelligence with Caption Studio

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.

Reviewing recorded interviews for affective cues such as composure, hesitation and agitation is slow and subjective, and cloud services that could automate it require sensitive audio to leave the device. EmotionAI is a fully local Computational Intelligence (CI) pipeline that couples Speech Emotion Recognition (SER) with generative reasoning. Speaker diarisation, Whisper Automatic Speech Recognition (ASR) and a wav2vec2 emotion classifier produce per-segment affective evidence, which is then passed to an adversarial three-model local Large Language Model (LLM) panel for timestamp-grounded and citation-constrained question answering. Zero-shot evaluation on the RAVDESS four-class English subset (n = 672) exposes cross-corpus fragility rather than classifier superiority: the deployed classifier scores 48.8% accuracy, above random (24.9%) and majority (28.6%) baselines but below an in-domain MFCC + logistic-regression comparator (71.0%). The complete pipeline runs in a mean 157 s on CPU (real-time factor approximately 1.33) with zero external calls. The contribution is not state-of-the-art SER but an auditable, privacy-preserving integration of imperfect affective evidence into grounded conversational analysis, together with an honest empirical account of where cross-corpus transfer and human-centred validation still fall short.

Current audio foundation models typically rely on rigid, task-specific supervision, addressing isolated factors of audio rather than the whole. In contrast, human intelligence processes audio holistically, seamlessly bridging physical signals with abstract cognitive concepts to execute complex tasks. Grounded in this philosophy, we introduce Bagpiper, an 8B audio foundation model that interprets physical audio via rich captions, i.e., comprehensive natural language descriptions that encapsulate the critical cognitive concepts inherent in the signal (e.g., transcription, audio events). By pre-training on a massive corpus of 600B tokens, the model establishes a robust bidirectional mapping between raw audio and this high-level conceptual space. During fine-tuning, Bagpiper adopts a caption-then-process workflow, simulating an intermediate cognitive reasoning step to solve diverse tasks without task-specific priors. Experimentally, Bagpiper outperforms Qwen-2.5-Omni on MMAU and AIRBench for audio understanding and surpasses CosyVoice3 and TangoFlux in generation quality, capable of synthesizing arbitrary compositions of speech, music, and sound effects. To the best of our knowledge, Bagpiper is among the first works that achieve unified understanding generation for general audio. Model, data, and code are available at Bagpiper Home Page.