Abstract:
We present an approach to speaker identification using noisy
speech observations where the speech enhancement and speaker
identification tasks are performed jointly. This is motivated by
the belief that human beings perform these tasks jointly and that
optimality may be sacrificed if sequential processing is used.
We employ a Bayesian approach where the speech features are
modeled using a mixture of Gaussians prior. A Gibbs sampler
is used to estimate the speech source and the identity of the
speaker. Preliminary experimental results are presented comparing our approach to a maximum likelihood approach and
demonstrating the ability of our method to both enhance speech
and identify speakers.
