PNCC features and FNN - MAP compensation techniques for continuous speech recognition

One of the biggest problems of a speech recognition system is the signal degradation due to adverse conditions. Such situations usually lead to mismatch between the test conditions and the training data, caused by non-linear distortion. The authors propose a histogram mapping followed by a filter through neural networks techniques (based on the features compensation), in order to minimize the misfit caused by noise insertion in the speech signal. The proposed method has been evaluated using the TIMIT and Noisex-92 databases. Recognition results show that the histogram mapping combined with filter with neural networks in the field of the cepstral coefficients do improve the recognition rates.