speaker diarization

Speaker diarization identifies and segments audio recordings by speaker, answering the question "who spoke when" in multi-speaker conversations, meetings, interviews, podcasts, and other recordings. The output is a timeline of speaker segments — timestamps indicating which speaker is active during each portion of the audio, without necessarily knowing the speakers' identities (they are typically labeled as Speaker 1, Speaker 2, etc. unless combined with speaker identification). The traditional diarization pipeline consists of: voice activity detection (VAD — identifying speech versus silence/noise segments), speech segmentation (dividing audio into short uniform segments, typically 1-3 seconds), speaker embedding extraction (converting each segment into a fixed-dimensional vector representing the speaker's voice characteristics using models like x-vectors, d-vectors, or ECAPA-TDNN), clustering (grouping segments by speaker using spectral clustering, agglomerative hierarchical clustering, or other methods — segments from the same speaker should cluster together), and resegmentation (refining segment boundaries for more precise timestamps). Modern end-to-end approaches include: EEND (End-to-End Neural Diarization — using self-attention to jointly model all speakers and output frame-level speaker labels), EEND-EDA (extending EEND with encoder-decoder attractors for flexible speaker count handling), and PixIT and other recent transformer-based architectures. Key challenges include: overlapping speech (multiple speakers talking simultaneously — traditional pipeline approaches struggle with overlap, while EEND handles it naturally), unknown number of speakers (the system must determine how many speakers are present), short speaker turns (brief interjections are difficult to correctly attribute), and domain mismatch (models trained on meetings may perform poorly on telephone conversations). Applications span meeting transcription, call center analytics, media content indexing, legal deposition processing, and medical consultation documentation. Services like pyannote.audio, Whisper + diarization pipelines, and cloud APIs (Google, AWS, Azure) provide accessible implementations.