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How Should a Speech Recognizer Work?
Author(s) -
Scharenborg Odette,
Norris Dennis,
Bosch Louis,
McQueen James M.
Publication year - 2005
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog0000_37
Subject(s) - computer science , parallels , task (project management) , speech recognition , field (mathematics) , computational model , word (group theory) , relation (database) , speech processing , natural language processing , human communication , artificial intelligence , linguistics , communication , psychology , mechanical engineering , philosophy , mathematics , management , database , pure mathematics , engineering , economics
Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational‐level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken‐word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input.