Abstract for reinhard_ann97

IEE Artificial Neural Networks (ANN 97)


K. Reinhard and M. Niranjan

July 1997

Modelling context effects and segmental transitions in speech recognition systems is very important. Explicitly modelling segmental transitions in a RNN framework would circumvent these problems. We present an interesting application of {\em Principal Curves}an algorithm to extract a non-linear summary of p-dimensional data firstly published in 1989 by Hastie/Stuetzle. The algorithm can be used to visualize non-linear transient characteristics in speech. We will show that between-phone characteristics found within diphones can be used as discriminant information to distinguish ambiguous phones. The technique used is explained and illustrated on the examples /{\it bah}/, /{\it dah}/ and /{\it gah}/.

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