Speaker normalization for template based speech recognition

Vocal Tract Length Normalization (VTLN) has been shown to be an efficient speaker normalization tool for HMM based systems. In this paper we show that it is equally efficient for a template based recognition system. Template based systems, while promising, have as potential drawback that templates maintain all non phonetic details apart from the essential phonemic properties; i.e. they retain information on speaker and acoustic recording circumstances. This may lead to a very inefficient usage of the database. We show that after VTLN significantly more speakers - also from opposite gender - contribute templates to the matching sequence compared to the non-normalized case. In experiments on the Wall Street Journal database this leads to a relative word error rate reduction of 10%.