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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. Spectral/temporal features which represent the smoothed trajectory of FFT 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.

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