Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Posterior Union Model with Applications to Robust Speech and Speaker Recognition | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article iD 75390 Pages 1-12 DOI ASP 2006 75390 A Posterior Union Model with Applications to Robust Speech and Speaker Recognition Ji Ming 1 Jie Lin 2 and F. Jack Smith1 1 School of Computer Science Queen s University Belfast Belfast BT7 INN UK 2 School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 610054 China Received 13 January 2005 Revised 12 December 2005 Accepted 14 December 2005 Recommended for Publication by Douglas O Shaughnessy This paper investigates speech and speaker recognition involving partial feature corruption assuming unknown time-varying noise characteristics. The probabilistic union model is extended from a conditional-probability formulation to a posteriorprobability formulation as an improved solution to the problem. The new formulation allows the order of the model to be optimized for every single frame thereby enhancing the capability of the model for dealing with nonstationary noise corruption. The new formulation also allows the model to be readily incorporated into a Gaussian mixture model GMM for speaker recognition. Experiments have been conducted on two databases TIDIGITS and SPIDRE for speech recognition and speaker identification. Both databases are subject to unknown time-varying band-selective corruption. The results have demonstrated the improved robustness for the new model. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Speech and speaker recognition systems need to be robust against unknown partial corruption of the acoustic features where some of the feature components may be corrupted by noise but knowledge about the corruption including the number and identities of the corrupted components and the characteristics of the corrupting noise is not available. This problem has been addressed recently by the missing-feature methods see . 1-10 .
