کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6961037 | 1452028 | 2015 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Autonomous selection of i-vectors for PLDA modelling in speaker verification
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Recently, systems combining i-vector and probabilistic linear discriminant analysis (PLDA) have become one of the state-of-the-art methods in text-independent speaker verification. The training data of a PLDA model is often collected from a large, diverse population. However, including irrelevant or noisy training data may deteriorate the verification performance. In this paper, we first show that data selection using k-NN improves the speaker verification performance. We then present a robust way of selecting k based on the local distance-based outlier factor (LDOF). We call this method flexible k-NN (fk-NN). We conduct experiments on male and female trials of several telephone conditions of the NIST 2006, 2008, 2010 and 2012 Speaker Recognition Evaluations (SRE). By using fk-NN, we discard a substantial amount of irrelevant or noisy training data without depending on tuning k, and achieve significant performance improvements on the NIST SRE sets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Speech Communication - Volume 72, September 2015, Pages 32-46
Journal: Speech Communication - Volume 72, September 2015, Pages 32-46
نویسندگان
Sangeeta Biswas, Johan Rohdin, Koichi Shinoda,