Creating hidden Markov models for fast speech by optimized clustering
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چکیده
Previous studies have shown that the recognition accu racy often severely degrades at higher speech rates which can basically be traced back to two main dimensions acoustic and phonemic Reasons for this e ect can be found in the phonemic eld e g elisions as well as on the acoustic level with increasing rates of speech the spec tral characteristics are changing A main obstacle in this context is the training data consisting of only a small fraction of samples which can be labeled as fast There fore the e ects caused by an increased speech rate often cannot be completely covered To meet this problem in this paper an optimized clustering process is presented making e cient use of the available data Our modi ed mixture splitting algorithm with an incorporated cross validation step aims at increasing the generalization of Hidden Markov Models especially with respect to fast speech Experimental results showed a relative decrease in word error rate of for fast speech
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تاریخ انتشار 1999