Adaptation of Statistical Language Models for Automatic Speech Recognition

Adaptation of Statistical Language Models for Automatic Speech Recognition (1999)

by Philip R. Clarkson

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Datum	Value	Source
TITLE	Adaptation of Statistical Language Models for Automatic Speech Recognition	SVM HeaderParse 0.1
AUTHOR NAME	Philip R. Clarkson	SVM HeaderParse 0.1
AUTHOR AFFIL	Darwin College, University of Cambridge,; and; Cambridge University Engineering Department.; Dissertation submitted to the University of Cambridge	SVM HeaderParse 0.2
ABSTRACT	Statistical language models encode linguistic information in such a way as to be useful to systems which process human language. Such systems include those for optical character recognition and machine translation. Currently, however, the most common application of language modelling is in automatic speech recognition, and it is this that forms the focus of this thesis. Most current speech recognition systems are dedicated to one specific task (for example, the recognition of broadcast news), and thus use a language model which has been trained on text which is appropriate to that task. If, however, one wants to perform recognition on more general language, then creating an appropriate language model is far from straightforward. A taskspecific language model will often perform very badly on language from a different domain, whereas a model trained on text from many diverse styles of language might perform better in general, but will not be especially well suited to any particular domai...	user correction - Legacy Corrections
YEAR	1999	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	PhD thesis	INFERENCE
CITATIONS	52 found	ParsCit 1.0