  This paper describes a method for overlap-aware speaker diarization. Given an
overlap detector and a speaker embedding extractor, our method performs
spectral clustering of segments informed by the output of the overlap detector.
This is achieved by transforming the discrete clustering problem into a convex
optimization problem which is solved by eigen-decomposition. Thereafter, we
discretize the solution by alternatively using singular value decomposition and
a modified version of non-maximal suppression which is constrained by the
output of the overlap detector. Furthermore, we detail an HMM-DNN based overlap
detector which performs frame-level classification and enforces duration
constraints through HMM state transitions. Our method achieves a test
diarization error rate (DER) of 24.0% on the mixed-headset setting of the AMI
meeting corpus, which is a relative improvement of 15.2% over a strong
agglomerative hierarchical clustering baseline, and compares favorably with
other overlap-aware diarization methods. Further analysis on the LibriCSS data
demonstrates the effectiveness of the proposed method in high overlap
conditions.
