| Paper: | SLP-P19.1 | ||
| Session: | Model-based Robust Speech Recognition | ||
| Time: | Friday, May 19, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Model-based robust Speech Recognition | ||
| Title: | Modeling Variance Variation in a Variable Parameter HMM Framework for Noise Robust Speech Recognition | ||
| Authors: | Xiaodong Cui, University of California, Los Angeles, United States; Yifan Gong, Microsoft Corporation, United States | ||
| Abstract: | Variance variation with respect to a continuous environment-dependent variable is investigated in this paper in a variable parameter Gaussian mixture HMM (VP-GMHMM) for noisy speech recognition. The variation is modeled by a scaling polynomial applied to the variances in the conventional hidden Markov acoustic models. The maximum likelihood estimation of the scaling polynomial is performed under an SNR quantization approximation. Experiments on the Aurora 2 database show significant improvements by incorporating the variance scaling scheme into the previous VP-GMHMM where only mean variation is considered. | ||
©2018 Conference Management Services, Inc. -||- email: webmaster@icassp2006.org -||- Last updated Friday, August 17, 2012