Representing Time in Automated Speech Recognition

Abstract

This thesis explores the treatment of temporal information in Automated Speech Recognition. It reviews the study of time in speech perception and concludes that while some temporal information in the speech signal is of crucial value in the speech decoding process not all temporal information is relevant to decoding. We then review the representation of temporal information in the main automated recognition techniques: Hidden Markov Models and Artificial Neural Networks. We find that both techniques have difficulty representing the type of temporal information that is phonetically or phonologically significant in the speech signal. ¶ ... Show more