Modeling of the Pstn Channel and Multireferences Training in Robust Speech Recognition

The telephone channel triggers, by the reduction of the signal bandwidth, a drop of the performances of most of the recognition systems which belong to speaker identification or continuous speech recognition. Many compensation techniques have been developed to reduce the unmatching issue between the training and the test databases which is supposed to be the main cause of the results decrease. We can gather these techniques in two categories: (1) feature compensation in which the representation of the acoustic vector is adjusted, and (2) model adaptation in which the HMM parameters are modified to get closer of the testing environments. The developed method is in the second one. The main purpose of this paper is to develop a modeling of the PSTN channel in order to train HMM with the database TIMIT passed through the PSTN channel modeling. NTIMIT was used for testing. Comparisons will be made by the way of feature compensation techniques joined with the proposed approach