Microsoft Research, Microsoft Corporation 
Oct 14 1997


The Contribution of Search to Automatic Speech Recognition



Abstract
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Since the earliest days of computing automatic speech recognition
technology has ridden the technology wave that has come to be known as
Moore's Law. This is vividly illustrated by the market introduction of
several general-purpose continuous speech recognition products. Besides
Moore's law the two things that have made this possible have been
advances in acoustic modeling, especially adaptation technologies and
advances in decoding techniques that permit real-time performance on
today's PC's.

I will discuss a broad range of these decoding techniques including,
beam search, A-star and variants, multi-pass search, incremental
application of knowledge, tree-organization, heuristic pruning and
look-ahead techniques. Additionally I will discuss the relative merits
of these search techniques with respect to memory size and performance.

Biography
Fil Alleva received the BS degree in mathematics from Carnegie Mellon
University in 1980. As an undergraduate he worked on the Harpy speech
recognition system. Later as a Project Scientist at CMU he contributed
to the Agora, Sphinx I and Sphinx II system, for which he was awarded
the 1992 Allen Newell Research Excellence Medal. He joined Microsoft
Research in 1993 and is currently managing CSR system development. Mr.
Alleva is a member of the IEEE and has published numerous papers on
spoken language technology. His current professional interests are in
all areas of spoken language technology, particularly heuristic search
and language modeling. 

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