摘要：This study presents new contribution towards the Adaptive Temporal Radial Basis Function (ATRBF) applied to Continuous speech recognition, in particular the recognition of phonemes like Timit Corpus. ATRBF combines features from Time Delay Neural Network (TDNN) and the advantages of Radial Basis Function (RBF). The capacity to detect the acoustic features and their independent temporal report of the temporal localisation is inspired from the TDNN model. The main use of RBF is both their speed of treatment and few parameters to adjust for the training phase, which encourages to apply this model to new tasks in most delicate cases.
