Temporal patterns (TRAPs) in ASR of noisy speech

Abstract

We study a new approach to processing temporal information for automatic speech recognition (ASR). Specifically, we study the use of rather long-time temporal patterns (TRAPs) of spectral energies in place of the conventional spectral patterns for ASR. The proposed neural TRAPs are found to yield significant amount of complementary information to that of the conventional spectral feature based ASR system. A combination of these two ASR systems is shown to result in improved robustness to several types of additive and convolutive environmental degradations.