Vulnerabilities to spoofing, also known as presentation attacks, are one exam- ple which refers .... In digital audio signal processing applications, time-frequency.
AN EFFICIENT FEATURE SELECTION METHOD FOR SPEAKER. RECOGNITION. Hanwu Sun, Bin Ma and Haizhou Li. Institute for Infocomm Research.
Genetic Algorithm Based Feature Selection for Speaker Trait Classification. Dongrui Wu. Machine Learning Lab, GE Global
humans rely not only on the low-level acoustic information but also on ... Systems Engineering and Engineering Managemen
in practical applications possible. Nevertheless, large differ- ences in terms of performance are observed, depending on the speaker or the speech excerpt used.
Jun 28, 2012 - especially suitable in real-life scenarios or when a remote recognition over the .... design is implemented on a low-cost Spartan 3 FPGA. The.
May 10, 2017 - task [10]. They constructed a DNN model with 496 speak- ers in the ... SD] 10 May 2017 .... As in [10], the utterance-level representation of a.
preprocessing, feature extraction, classification and followed by the actual .... character patterns in scanned digital image. ...... Indexing of Online Signatures.
enhance the PSK in terms of accuracy and computational ... In these generative approaches the training ... speech utterances for text-independent speaker verification. ..... [16] S. Haykin, Neural Network: A Comprehensive Foundation. NJ:.
transformation converts the saliency measure to decibels [32]. ANN-SNR ..... http://securityaffairs.co/wordpress/14641/cyber-crime/us-critical-infrastructure-under-cyber-attacks.html. ... Available: http://tech911.info/html/body_it_training_help.html
Keywords: Component; Feature Selecrion Methods; Data Mining; Breast ... Nowadays, we are capable to collect and generate data more than before. .... Weka provides the environment to perform many machine learning algorithm and feature.
The CDX network traffic data under analysis was collected at AFIT [43] and was captured in the libpcap file format .... Training methods, such as backpropagation (used here), are used to ...... Business Analytics and Optimization, 1st ed., vol.
Electronics Department, Faculty of engineering science. Badji Mokhtar .... in text-independent speaker verification, in which can be .... Signature trajectories are then pre-processed by subtracting the ... the upper dot notation represents an approx
him from other speakers. This was supported by preliminary past work [1]. Feature selection is the process of selecting a features subset, which is most effective ...
ON FEATURE SELECTION FOR SPEAKER VERIFICATION Arnon Cohen and Yaniv Zigel Electrical and Computer Engineering Department, Ben-Gurion University, Beer-Sheva, Israel arnon(yaniv)@ee.bgu.ac.il
ABSTRACT This paper describes an HMM based speaker verification system, which verifies speakers in their own specific feature space. This ‘individual’ feature space is determined by a Dynamic Programming (DP) feature selection algorithm. A suitable criterion, correlated with Equal Error Rate (EER) was developed and is used for this feature selection algorithm. The algorithm was evaluated on a text-dependent database. A significant improvement in verification results was demonstrated with the DP selected individual feature space. An EER of 4.8% was achieved when the feature set was the “almost standard” Mel Frequency Cepstrum Coefficients (MFCC) space (12 MFCC + 12 ∆MFCC). Under the same conditions, a system based on the selected feature space yielded an EER of only 2.7%.
1. INTRODUCTION Today, Automatic Speaker Verification (ASV) systems use a common feature space for all speakers. Moreover, this common set of features is most often the set of cepstral and delta-cepstral coefficients, used in speech recognition tasks. Very little work has been done on selecting the optimal feature space for speaker verification/identification tasks [1, 2, 5-9]. The main idea in our research is that every speaker has his own ‘optimal’ feature space, which optimally discriminates him from other speakers. This was supported by preliminary past work [1]. Feature selection is the process of selecting a features subset, which is most effective for preserving class separability. The feature selection method can be specified in terms of two components: 1) A performance criterion 2) Selection procedure, The problem of feature selection can be described as follows: Given a set y of K features y = { yi | i = 1, 2,..., K } select a subset x (of k
