Representing Time in Automated Speech Recognition
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...[Show more]
|Collections||Open Access Theses|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.