A space-perturbance/time-delay neural network for speech recognition

Abstract

The authors present a space-perturbance time-delay neural network (SPTDNN), which is a generalization of the time-delay neural network (TDNN) approach. It is shown that by introducing the space-perturbance arrangement, the SPTDNN has the ability to be robust to both temporal and dynamic acoustic variance of speech features, thus, is a potentially component approach to speaker-independent and/or noisy speech recognition. The authors introduce the architecture, learning algorithm, and theoretical evaluation of the SPTDNN, along with experimental results. Experimental comparisons show that the SPTDNN obtains a performance that improves upon the TDNN for both speaker-dependent/-independent and noisy phoneme recognition.<>