| Paper: | SP-P12.7 | ||
| Session: | Acoustic Modeling: Model Complexity, General Topics | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech Processing: Acoustic Modeling for Speech Recognition | ||
| Title: | EXTENDED BAUM TRANSFORMATIONS FOR GENERAL FUNCTIONS | ||
| Authors: | Dimitri Kanevsky; IBM T. J. Watson Research Center | ||
| Abstract: | The discrimination technique for estimating the parameters of Gaussian mixtures that is based on the Extended Baum transformations (EB) has had significant impact on the speech recognition community. There appear to be no published proofs that definitively show that these transformations increase the value of an objective function with iteration (i.e., so-called ''growth transformations''). The proof presented in the current paper is based on the linearization process and the explicit growth estimate for linear forms of Gaussian mixtures. We also derive new transformation formulae for estimating the parameters of Gaussian mixtures generalizing the EB algorithm, and run simulation experiments comparing different growth transformations. | ||
| Back | |||
Home -||-
Organizing Committee -||-
Technical Committee -||-
Technical Program -||-
Plenaries
Paper Submission -||-
Special Sessions -||-
ITT -||-
Paper Review -||-
Exhibits -||-
Tutorials
Information -||-
Registration -||-
Travel Insurance -||-
Housing -||-
Workshops