Dimension Reduction of Speech Emotion Feature Based on Weighted Linear Discriminant Analysis

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Emotion clustering based on probabilistic linear discriminant analysis

This study proposes an emotion clustering method based on Probabilistic Linear Discriminant Analysis (PLDA). Each emotional utterance is modeled as a GMM mean supervector. Hierarchical clustering is applied to cluster supervectors that represent similar emotions using a likelihood ratio from a PLDA model. The PLDA model can be trained with a different emotional database from the test data, with...

متن کامل

Feature Transformation Based on Generalization of Linear Discriminant Analysis

Hidden Markov models (HMMs) have been widely used to model speech signals for speech recognition. However, they cannot precisely model the time dependency of feature parameters. In order to overcome this limitation, several researchers have proposed extensions, such as segmental unit input HMM (Nakagawa & Yamamoto, 1996). Segmental unit input HMM has been widely used for its effectiveness and t...

متن کامل

Weighted Linear Discriminant Analysis based on Class Saliency Information

In this paper, we propose a new variant of Linear Discriminant Analysis to overcome underlying drawbacks of traditional LDA and other LDA variants targeting problems involving imbalanced classes. Traditional LDA sets assumptions related to Gaussian class distribution and neglects influence of outlier classes, that might hurt in performance. We exploit intuitions coming from a probabilistic inte...

متن کامل

Boosting Weighted Linear Discriminant Analysis

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows to select the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

متن کامل

Feature Reduction for Intrusion Detection Using Linear Discriminant Analysis

Intrusion detection is one of core technologies of computer security. It is required to protect the security of computer network systems. Most of existing IDs use all features in the network packet to look for known intrusive patterns. Some of these features are irrelevant or redundant. A well-defined feature extraction algorithm makes the classification process more effective and efficient. Th...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: International Journal of Signal Processing, Image Processing and Pattern Recognition

سال: 2015

ISSN: 2005-4254,2005-4254

DOI: 10.14257/ijsip.2015.8.11.27