Discriminant neural predictive coding applied to phoneme recognition
In this article, we propose to study a speech coding method applied to the recognition of phonemes. The model proposed (the neural predictive coding (NPC) and its three declinations NPC-1, NPC-2 and DFENPC) is a connectionist model (multilayer perceptron) based on the nonlinear prediction of the speech signal. We show that it is possible to improve the discriminant capacities of such an encoder with the introduction of signal membership class information as from the coding stage. As such, it fits in with the category of discriminant features extraction (DFE) encoders already proposed in literature. In this study we present a theoretical validation of the model in the hypothesis of unnoised signals and Gaussian noised signals. We also define a new distance, the NPC distance, that will allow experimental validation of the model. NPC performances are compared to that obtained with traditional methods used to process speech on the Darpa Timit phoneme base. Simulations presented here show that the classification rates have clearly improved compared to usual methods, in particular regarding phonemes considered difficult to process (/b/,/d/,/g/ and /p/,/t/,/k/ phonemes).
speech coding discriminant feature extraction neural networks nonlinear signal processing