A new paradigm for speaker-independent training

A paradigm for speaker-independent (SI) training of hidden Markov models (HMMs) is presented for continuous speech recognition. The method uses a large amount of speech from a few speakers instead of the traditional practice of using a little speech from many speakers. Furthermore, it avoids the common practice of pooling speech data from many speakers prior to training a SI model. The approach has been tested using the BBN Byblos system on the DARPA Resource Management corpus under standard test conditions. With only 12 speakers for training the SI models, recognition performance. comparable to that reported for other systems using 109 training speakers has been achieved. Besides surprisingly good recognition performance, this method offers many practical advantages that have implications for the way speech corpora, are designed and used for training SI models