PHONE CLASSIFICATION WITH SEGMENTAL FEATURES AND CLASSIFIER A BINARY-PAIR PARTITIONED NEURAL NETWORK
Stephen A. Zahorian, Peter L. Silsbee, Xihong Wang · 1997
This paper presents methods and experimental results for phonetic classification using 39 phone classes and the NIST recommended training and test sets for NTIMIT and TIMIT. Spectrdtemporal features which represent the smoothed trajectory of FlT derived speech spectra over 300 ms intervals are used for the analysis. Classification tests are made with both a binary-pair partitioned (BPP) neural network system (one neural network for each of the 741 pairs of phones) and a single large neural network. Classification accuracy is very similar for the two types of networks, but the BPP method has the advantage of much less training time. The best results obtained (77% for TIMIT and 67.4% for NTIMIT) compare favorably to the best results reported in the literature for this task.