|
naistar (NAIST Academic Repository) >
学術リポジトリ naistar / NAIST Academic Repository naistar >
国際会議発表論文 / Proceedings >
情報科学研究科 / Graduate School of Information Science >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10061/8040
|
| Title: | Gaussian Mixture Selection Using Context-Independent HMM |
| Authors: | Akinobu Lee
Tatsuya Kawahara
Kiyohiro Shikano |
| Issue Date: | May-2001 |
| Publisher: | IEEE |
| Start page: | 69 |
| End page: | 72 |
| Abstract: | We address a method to efficiently select Gaussian mixtures for fast acoustic likelihood computation. It makes use of context-independent models for selection and back-off of corresponding triphone models. Specifically, for the k-best phone models by the preliminary evaluation, triphone models of higher resolution are applied, and others are assigned likelihoods with the monophone models. This selection scheme assigns more reliable back-off likelihoods to the un-selected states than the conventional Gaussian selection based on a VQ codebook. It can also incorporate efficient Gaussian pruning at the preliminary evaluation, which offsets the increased size of the pre-selection model. Experimental results show that the proposed method achieves comparable performance as the standard Gaussian selection, and performs much better under aggressive pruning condition. Together with the phonetic tied-mixture modeling, acoustic matching cost is reduced to almost 14% with little loss of accuracy |
| Description: | ICASSP2001: IEEE International Conference on Acoustics, Speech and Signal Processing, May 7-11, 2001, Salt Lake City, Utah, US. |
| URI: | http://hdl.handle.net/10061/8040 |
| ISBN: | 0780370414 |
| ISSN: | 1520-6149 |
| Rights: | Copyright 2001 IEEE |
| Text Version: | Publisher |
| Publisher DOI: | 10.1109/ICASSP.2001.940769 |
| Appears in Collections: | 情報科学研究科 / Graduate School of Information Science
|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
|
