Speaker Recognition and Diarization

Speaker recognition produces identity-related similarity scores from speech samples. Identification, verification and diarization are different decision problems.

Classical approaches

GMM-UBM systems compare a speaker distribution with a universal background model. The i-vector approach represents speech in a compact total-variability space. Methods such as PLDA score similarity between these representations.

Learned representations

Modern systems use x-vectors and related speaker embeddings. Training encourages samples from the same speaker to be close while separating different speakers.

Speaker recognition converts a continuous score into accept or reject decisions using a threshold. Channel mismatch, codec, utterance duration and background noise can shift score distributions.

Speaker diarization

Speaker diarization answers “who spoke when?” by combining VAD, segment embeddings and clustering. Unknown speaker count and overlapping speech remain difficult conditions.

A speaker score is not a deterministic identity marker; it should be interpreted together with threshold calibration, recording conditions and measured error rates.