NAP for high level language identification
May 22, 2011
Conference Paper
Author:
Published in:
ICASSP 2011, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 22-27 May 2011, pp. 4392-4395.
R&D Area:
Summary
Varying channel conditions present a difficult problem for many speech technologies such as language identification (LID). Channel compensation techniques have been shown to significantly improve performance in LID for acoustic systems. For high-level token systems, nuisance attribute projection (NAP) has been shown to perform well in the context of speaker identification. In this work, we describe a novel approach to dealing with the high dimensional sparse NAP training problem as applied to a 4-gram phonotactic LID system run on the NIST 2009 Language Recognition Evaluation (LRE) task. We demonstrate performance gains on the Voice of America (VOA) portion of the 2009 LRE data.