Abstract
Due to their effectiveness and efficiency for user-independent recognition, hidden Markov models (HMMs) are widely used in applications such as speech recognition (word recognition, connected word recognition and continuous speech recognition), lip-reading and gesture recognition. Output probability computations (OPCs) of continuous HMMs and likelihood scorer computations (LSCs) are the most time-consuming part of HMM-based recognition systems.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
