Automatic Generation of Hypotheses for Automatic Diagnosis of Pronunciation Errors
نویسندگان
چکیده
This paper describes the use of a rule based system for generation of pronunciation variants as a component of a speech-enabled computer aided pronunciation learning (CAPL) system. This CAPL system is a part of a computer aided recitation of the holy Qur an training system. It generates the most probable pronunciation error hypotheses that are fed to a hidden Markov model (HMM)-based speech recognizer in order to test them against the spoken utterance. It also generates mapping information to determine the appropriate location for the feedback of each candidate hypothesis.
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