Decision tree distribution tying based on a dimensional split technique
نویسندگان
چکیده
In this paper, a new clustering technique called Dimensional Split Phonetic Decision Tree (DS-PDT) is proposed. In DSPDT, state distributions are split dimensionally when applying phonetic question. This technique is an extension of the decision tree based acoustic modeling. It gives a proper context-dependent sharing structure of each dimension automatically while maintaining the correlations among the dimensions. In speaker-independent continuous speech recognition experiments, DS-PDT achieved about 8% error reduction over the phonetic decision tree clustering.
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In this paper, a new clustering technique called Dimensional Split Phonetic Decision Tree (DS-PDT) is proposed. In DSPDT, state distributions are split dimensionally when applying phonetic question. This technique is an extension of the decision tree based acoustic modeling. It gives a proper context-dependent sharing structure of each dimension automatically while maintaining the correlations ...
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تاریخ انتشار 2002