Unsupervised rapid speaker adaptation based on selective eigenvoice merging for user-specific voice interaction

Title
Unsupervised rapid speaker adaptation based on selective eigenvoice merging for user-specific voice interaction
Author(s)
박정식최동진[최동진]오영환[오영환]
Keywords
SPEECH RECOGNITION; ROBOTS
Issue Date
201504
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, v.40, pp.95 - 102
Abstract
Speaker adaptation transforms the standard speaker-independent acoustic models into an adapted model relevant to the user (called the target speaker) in order to provide reliable speech recognition performance. Although several conventional adaptation techniques, such as Maximum Likelihood Linear Regression (MLLR) and Maximum A Posteriori (MAP), have been successfully applied to speech recognition tasks, they demonstrate great dependency on the amount of adaptation data. However, the eigenvoice-based adaptation technique is known to provide reliable performance regardless of the amount of data, even for a very small amount In this study, we propose an efficient eigenvoice adaptation approach to construct more reliable adapted models. The proposed approach merges eigenvoice sets for possible eigenvoice combinations, and then selects optimal eigenvoice sets that are most relevant to the target speaker. For this task, we propose an efficient unsupervised eigenvoice selection method as well as a rapid merging technique. On speech recognition experiments using the Defense Advanced Research Projects Agency's Resource Management corpus, the proposed approach exhibited superior performance, compared to conventional methods, in both recognition accuracy and time complexity. (C) 2015 Elsevier Ltd. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/32794http://dx.doi.org/10.1016/j.engappai.2015.01.010
ISSN
0952-1976
Appears in Collections:
공과대학 > 모바일정보통신공학과 > Articles
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