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要旨:
This paper investigates the interaction between acoustic
scores and symbolic mismatch penalties in multi-pass speech
decoding techniques that are based on the creation of a
segment graph followed by a lexical search. The interaction
between acoustic and symbolic mismatches determines to a
large extent the structure of the search space of these multipass
approaches. The background of this study is a recently
developed computational model of human word recognition,
called SpeM. SpeM is able to simulate human word
recognition data and is built as a multi-pass speech decoder.
Here, we focus on unravelling the structure of the search
space that is used in SpeM and similar decoding strategies.
Finally, we elaborate on the close relation between distances
in this search space, and distance measures in search spaces
that are based on a combination of acoustic and phonetic
features.
