توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱MLLR method for Environmental Adaptation in a Continuous Farsi Speech Recognition
نویسنده(ها): ، ،
اطلاعات انتشار: ششمین کنفرانس سراسری سیستم های هوشمند، سال
تعداد صفحات: ۷
In this paper, MLLR1 adaptation of continuous density HMM2 is investigated in a Farsi speaker independent large vocabulary continuous speech recognition system in attempt to improve recognition rate in real world situations. In the MLLR framework, we have experienced the use of Gaussian mean transformations in global adaptation andregression tree based adaptation. Besides full and block–diagonal transformations of Gaussian means, transformation of Gaussian variances is examined. We have used MLLR technique in batch–supervised fashion since it is more beneficial in situations of severe mismatch. Our experiments on 4 different tasks, show that by using this technique the system recognition performance in a new environment can be significantly improved<\div>
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