کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
865674 | 909678 | 2008 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Maximum Likelihood A Priori Knowledge Interpolation-Based Handset Mismatch Compensation for Robust Speaker Identification
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی (عمومی)
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Unseen handset mismatch is the major source of performance degradation in speaker identification in telecommunication environments. To alleviate the problem, a maximum likelihood a priori knowledge interpolation (ML-AKI)-based handset mismatch compensation approach is proposed. It first collects a set of handset characteristics of seen handsets to use as the a priori knowledge for representing the space of handsets. During evaluation the characteristics of an unknown test handset are optimally estimated by interpolation from the set of the a priori knowledge. Experimental results on the HTIMIT database show that the ML-AKI method can improve the average speaker identification rate from 60.0% to 74.6% as compared with conventional maximum a posteriori-adapted Gaussian mixture models. The proposed ML-AKI method is a promising method for robust speaker identification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Tsinghua Science & Technology - Volume 13, Issue 4, August 2008, Pages 528-532
Journal: Tsinghua Science & Technology - Volume 13, Issue 4, August 2008, Pages 528-532
نویسندگان
Yuanfu (å»å
ç«), Zhixian (åºæºæ¾), Jyhher (æ¨æºå),