Preprocessing and neural classification of English stop consonants [b, d, g, p, t, k]

Neural networks are accepted as powerful learning tools in pattern recognition in which they proved their performance. Nevertheless, many problems like phoneme classification with multi-speaker continuous speech database are hard even for Neural Networks. Our aim is to propose an Artificial Neural Network architecture that detects acoustic features in speech signals and classifies them correctly. We reached this goal with English stop consonants [b, d, g, p, t, k] extracted from the general multi-speaker database (TIMIT) by modifying some parameter values in the preprocessing algorithm and by using a modified TDNN (Time Delay Neural Network) architecture. Our net performed a good classification giving as testing recognition percentage the following results: 92.9 for [b], 91.8 for [d], 92.4 for [g], 80.3 for [p], 90.2 for [t], 94.2 for [k].