【摘 要】
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This paper investigates the use of multi-distribution Deep Neural Networks(DNNs)for mispronunciation detection and diagnosis(MD&D).Our existing approach uses extended recognition networks(ERNs)to cons
【出 处】
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第七届国际博士生论坛暨青年教师发展研讨会
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This paper investigates the use of multi-distribution Deep Neural Networks(DNNs)for mispronunciation detection and diagnosis(MD&D).Our existing approach uses extended recognition networks(ERNs)to constrain the recognition paths to the canonical pronunciation of the target words and the likely phonetic mispronunciations.Although this approach is viable,it has some problems:(1)deriving appropriate phonological rules to generate the ERNs remains a challenging task;(2)the acoustic model(AM)and the phonological rules are trained independently and hence contextual information is lost; and(3)phones missing from the ERNs cannot be recognized even if we have a well-trained AM.
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