Hidden-articulator Markov models for pronunciation evaluation
Tepperman, J.; Narayanan, S.
Automatic Speech Recognition and Understanding, 2005 IEEE Workshop on
Volume , Issue , 27-27 Nov. 2005 Page(s):174 - 179
Digital Object Identifier   10.1109/ASRU.2005.1566471
Summary:The design of a robust language-learning system, intended to help students practice a foreign language along with a machine tutor, must provide for localization of common pronunciation errors. This paper presents a new technique for unsupervised detection of phone-level mispronunciations, created with language-learning applications in mind. Our method uses multiple hidden-articulator Markov models to asynchronously classify acoustic events in various articulatory domains. It requires no human input besides a pronunciation dictionary for all words in the end system's vocabulary, and has been shown to perform as well as a human tutor would, given the same task. For the majority of systematic mispronunciations investigated in this study, precision in detecting the presence of an error exceeded the 70% inter-annotator agreement reported by our test corpus

» View citation and abstract

Log in by entering your IEEE Web Account Username and Password.

IEEE Communications Society members: If you subscribe to the IEEE Electronic Periodicals Package or IEEE Electronic Periodicals Package Plus, you must access your subscription at www.comsoc.org.

Check with your librarian, information professional, or system manager to determine if you need to log in. Please complete the online Technical Support Form if you need assistance.

Select the Purchase History link to access the document. You will have 5 Days after purchase to access the Full Text PDF. Please complete the online Technical Support Form if you need assistance.

• Search and access Abstract records free of charge
• Register for table of contents alerts
• Purchase Full Text PDF documents

» Learn more about subscription options or how to become an IEEE Member.