Automatic Learning of a Production Rule System for Acoustic-Phonetic Decoding

Results are reported of experiments in the use of Charade, automatic learning of a production rule system, for acoustic-phonetic decoding. The Charade system is evaluated in terms of performance to classify phonetic macro-classes from generated production rules. As points of comparison, the same experiments are carried out with an usual classifier (i.e., Hamming Distance Nearest Neighbor) and a neural net based technique (i.e., Modified Hopfied Net). The results can be summarized as follows: For a given reasonable error rate, Charade classifier gives a higher accuracy rate than HDNN and performs as well as MHN.The generated production rules can be analysed and interpreted for knowledge acquisition.