SPEAKER TRACKING AND DETECTION WITH MULTIPLE SPEAKERS

We describe a speaker tracking and detection system, for Switchboard conversations, that uses a two-speaker and silence hidden Markov model (HMM) with a minimum state duration constraint and Gaussian mixture model (GMM) state distributions adapted from a single gender- and handset-independent imposter model distribution. Speaker tracking is used to segment speakers for detection, which is carried out by averaging frame scores of the Viterbi path and HNORM’ing via a novel parameter interpolation extension of HNORM for use with files of arbitrary lengths. Use of duration statistics augmenting the acoustic scores is also introduced via a nonlinear combination function. Results are reported on the NIST 1998 Multispeaker development evaluation dataset.