Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modelling errors in automatic speech recognition for dysarthric speakers
EURASIP Journal on Advances in Signal Processing - Special issue on analysis and signal processing of oesophageal and pathological voices
CanSpeak: a customizable speech interface for people with dysarthric speech
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs: Part I
ACM Transactions on Asian Language Information Processing (TALIP)
ICCHP'12 Proceedings of the 13th international conference on Computers Helping People with Special Needs - Volume Part II
Expert Systems with Applications: An International Journal
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A field of research in Automatic Speech Recognition (ASR) is the development of assistive technology, particularly for people with speech disabilities. Diverse techniques have been proposed to accomplish accurately this task, among them the use of Metamodels. In this paper we present an approach to improve the performance of Metamodels which consists in using a speaker's phoneme confusion matrix to model the pronunciation patterns of this speaker. In contrast with previous confusion-matrix approaches, where the confusion-matrix is only estimated with fixed settings for language model, here we explore on the response of the ASR for different language model restrictions. A Genetic Algorithm (GA) was applied to further balance the contribution of each confusion-matrix estimation, and thus, to provide more reliable patterns. When incorporating these estimates into the ASR process with the Metamodels, consistent improvement in accuracy was accomplished when tested with speakers of mild to severe dysarthria which is a common speech disorder.