Phone classification with segmental features and a binary-pair partitioned neural network classifier
Stephen A. Zahorian, Peter L. Silsbee, Xihong Wang · 2002
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. The classification accuracy is very similar for the two types of networks, but the BPP method has the advantage of a much shorter 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.