مشاهده مشخصات مقاله
Feature Selection and Dimension Reduction for Automatic Gender Identification
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نویسنده (ها) |
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Mohammad Ali Keyvanrad
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Mohammad Mehdi Homayounpour
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مربوط به کنفرانس |
چهاردهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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چکیده |
Gender identification based on speech signal has
become gradually a matter of concern in recent years.
In this context 6 feature types including MFCC, LPC,
RC, LAR, pitch values and formants are compared for
automatic gender identification and three best feature
types are selected using four feature selection
techniques. These techniques are GMM, Decision
Tree, Fisher’s Discriminant Ratio, and Volume of
Overlap Region. A dimension reduction is done on the
best three feature types and the best coefficients are
then selected from each feature vector. Selected
coefficients are evaluated for gender classification
using three types of classifiers including GMM, SVM
and MLP neural network. 96.09% gender
identification performance was obtained as the best
performance using the selected coefficients and MLP
classifier. |
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قیمت |
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برای اعضای سایت : ۱٠٠,٠٠٠ ریال
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برای دانشجویان عضو انجمن : ۲٠,٠٠٠ ریال
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برای اعضای عادی انجمن : ۴٠,٠٠٠ ریال
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خرید مقاله
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