1 Test normalization (T-Norm) is a score normalization technique that is regularly and successfully applied in the context of text-independent speaker recognition. It is less frequently applied, however, to text-dependent or textprompted speaker recognition, mainly because its improvement in this context is more modest. In this paper we present a novel way to improve the performance of T-Norm f...

The purpose of this study was to examine the results prediction breast cancer, which have been classified based on two types malignant and benign. method used in research is k-NN algorithm with normalization min-max Z-score, programming language R language. conclusion that highest k accuracy value = 5 21 an rate 98% using method. Whereas for Z-score at 15 97%. Thus considered better than Z-scor...

A popular score normalization technique termed T-norm is the central focus of this paper. Based on widely connrmed experimental observation regarding T-norm tilting the DET curves of speaker detection systems, we set out to identify the components taking role in this phenomenon. We claim that under certain local assumptions the T-norm performs a gaussianization of the individual true and impost...

A popular score normalization technique termed T-norm is the central focus of this paper. Based on widely confirmed experimental observation regarding T-norm tilting the DET curves of speaker detection systems, we set out to identify the components taking role in this phenomenon. We claim that under certain local assumptions the T-norm performs a gaussianization of the individual true and impos...

Quantifying the amount of polarization is crucial for understanding and studying political in social systems. Several methods are used commonly to measure networks by purely inspecting their structure. We analyse eight such show that all them yield high scores even random with similar density degree distributions typical real-world networks. Further, some sensitive relative sizes polarized grou...

INTEGRATION OF MULTIPLE CUES IN BIOMETRIC SYSTEMS By Karthik Nandakumar Biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. Unimodal biometric systems perform person recognition based on a single source of biometric information and are affected by problems like noisy sensor data, non-universality and lack of individuality...

Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reve...

Emotion mismatch between training and testing will cause system performance decline sharply which is emotional speaker recognition. It is an important idea to solve this problem according to the emotion normalization of test speech. This method proceeds from analysis of the differences between every kind of emotional speech and neutral speech. Besides, it takes the baseband mismatch of emotiona...

To address the challenges of normalizing online conversational texts prevalent in social media, we propose a contextual long-short term memory (LSTM) recurrent neural network based approach, augmented with a self-generated dictionary normalization technique. Our approach utilizes a sequence of characters as well as the part-of-speech associated with words without harnessing any external lexical...