ASR decoding in a computational model of human word recognition

Recently, a computational model of human word recognition, called SpeM, has been developed. In contrast to most current models of human word recognition, SpeM is able to process actual acoustic speech input, and decodes the incoming speech stream into lexical and non-lexical items. This model makes the links between HSR and ASR as explicit as possible. In this paper, we focus on unravelling the structure of the complex search space that is used in SpeM and similar decoding strategies. To that end, it discusses a number of properties of phone lattices in relation to canonical phone representations. Furthermore, 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.