Please use this identifier to cite or link to this item: doi:10.22028/D291-25309
Title: A category based approach for recognition of out-of-vocabulary words
Author(s): Gallwitz, Florian
Nöth, Elmar
Niemann, Heinrich
Language: English
Year of Publication: 1996
SWD key words: Künstliche Intelligenz
Free key words: artificial intelligence
DDC notations: 004 Computer science, internet
Publikation type: Report
Abstract: In almost all applications of automatic speech recognition, especially in spontaneous speech tasks, the recognizer vocabulary cannot cover all occurring words. There is always a significant amount of out-of-vocabulary words even when the vocabulary size is very large. In this paper we present a new approach for the integration of out-of-vocabulary words into statistical language models. We use category information for all words in the training corpus to define a function that gives an approximation of the out-of-vocabulary word emission probability for each word category. This information is integrated into the language models. Although we use a simple acoustic model for out-of-vocabulary words, we achieve a 6% reduction of word error rate on spontaneous speech data with about 5% out-of-vocabulary rate.
Link to this record: urn:nbn:de:bsz:291-scidok-53252
Series name: Vm-Report / Verbmobil, Verbundvorhaben, [Deutsches Forschungszentrum für Künstliche Intelligenz]
Series volume: 132
Date of registration: 13-Jun-2013
Faculty: SE - Sonstige Einrichtungen
Department: SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

Files for this record:
File Description SizeFormat 
report_132_96.pdf188,59 kBAdobe PDFView/Open

Items in SciDok are protected by copyright, with all rights reserved, unless otherwise indicated.