Robust Speech Recognition Using Temporal Pattern Feature Extracted From MTMLP Structure

Temporal Pattern feature of a speech signal could be either extracted from the time domain or via their front-end vectors. This feature includes long-term information of variations in the connected speech units. In this paper، the second approach is followed، i. e. the features which are the cases of temporal computations، consisting of Spectral-based (LFBE) and Cepstrum-based (MFCC) feature vectors، are considered. To extract these features، we use posterior probability-based output of the proposed MTMLP neural networks. The combination of the temporal patterns، which represents the long-term dynamics of the speech signal، together with some traditional features، composed of the MFCC and its first and second derivatives are evaluated in an ASR task. It is shown that the use of such a combined feature vector results in the increase of the phoneme recognition accuracy by more than 1 percent regarding the results of the baseline system، which does not benefit from the long-term temporal patterns. In addition، it is shown that the use of extracted features by the proposed method gives robust recognition under different noise conditions (by 13 percent) and، therefore، the proposed method is a robust feature extraction method.