  This paper proposes a novel joint multi-speaker tracking-and-separation
method based on the generalized labeled multi-Bernoulli (GLMB) multi-target
tracking filter, using sound mixtures recorded by microphones. Standard
multi-speaker tracking algorithms usually only track speaker locations, and
ambiguity occurs when speakers are spatially close. The proposed multi-feature
GLMB tracking filter treats the set of vectors of associated speaker features
(location, pitch and sound) as the multi-target multi-feature observation,
characterizes transitioning features with corresponding transition models and
overall likelihood function, thus jointly tracks and separates each
multi-feature speaker, and addresses the spatial ambiguity problem. Numerical
evaluation verifies that the proposed method can correctly track locations of
multiple speakers and meanwhile separate speech signals.
