Automatic prediction of linear frequency warp for speech recognition

In a template-based, speaker-independent, speech recognition system, stored templates may be used in matching the speech of new users. For optimal results, templates should be carefully selected and proper normalization algorithms should be applied for each new talker. This paper addresses the use of linear frequency warping for template normalization and describes both a technique for estimating the long-term distribution of the frequencies of a talker´s formants and a technique for automatically predicting an optimal linear frequency warp.