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Abstract
Several optimization techniques are vastly used today in the speech and language community for estimating model parameters. The Extended Baum-Welch (EBW) is one such technique that is extensively used for estimating the parameters of Gaussian mixture models based on a discriminative criteria (like Maximum Mutual Information). In this talk, we present EBW as a consistent, theoretical framework for parameter estimation and show how other common parameter estimation techniques (for example, based on Constrained Line Search) belong to this family of model update rules.We introduce a general family of parameter updates that generalizes a Baum-Welch recursive process to an arbitrary objective function of Gaussian Mixture Models or Poisson Processes.In the second part of this talk we introduce an extension of the EBW for estimating sparse signals from a sequence of noisy observations. As part of this, the underlining EBW algorithms are compared with recently introduced Kalman filtering-based compressed sensing methods.This is joint work with Avishy Carmi, David Nahamoo, Bhuvana Ramabhadran and Tara Sainath.
Biography
Dimitri Kanevsky is a research staff member in the Speech and Language algorithms department at IBM T.J.Watson Research Center. Prior to joining IBM, he worked at a number of prestigious centers for higher mathematics, including Max Planck Institute in Germany and the Institute for Advanced Studies in Princeton. At IBM he has been responsible for developing the first Russian automatic speech recognition system, as well as key projects for embedding speech recognition in automobiles and broadcast transcription systems. He currently holds 110 US patents and received a Master Inventor title at IBM. His conversational biometrics based security patent was recognized by MIT, Technology Review, as one of five most influential patents for 2003 and his work on Extended Baum-Welch algorithm in speech was recognized as 2002 science accomplishment by the Director of Research at IBM.