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https://arxiv.org/pdf/1009.4972.pdf
identify and verify human speech more accurately. This work presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). Mel-frequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network [8]. 2. Voice processing
https://pdfs.semanticscholar.org/82f3/0ec579faf5eee57aef683f672636447192ec.pdf
Speaker Recognition using Support Vector Machine Geeta Nijhawan ... identifying a speaker by machine using some characteristics of speaker’s voice [1]. ... and neural network techniques. We have used Support Vector Machine due to its high accuracy. 3.1. Support vector machine
https://link.springer.com/article/10.1023%2FA%3A1009715923555
Jun 01, 1998 · A Tutorial on Support Vector Machines for Pattern Recognition. Authors; ... M. Identifying speaker with support vector networks. In Interface ’96 Proceedings, Sydney, ... Schölkopf, B. and Müller, K.-R. The connection between regularization operators and support vector kernels. Neural Networks (to appear), 1998.Cited by: 21704
http://www.diva-portal.org/smash/get/diva2:561939/FULLTEXT02
for the support vector machine and testing it on a given set of breast cancer data. The support vector machine is then improved and generalized into higher dimensions, and used to solve a problem related to speaker recognition; in this case with parameters such as nasality. 2 Theory 2.1 Speech Processing
https://www.semanticscholar.org/paper/Probabilistic-speaker-identification-with-dual-Matsui-Tanabe/19429dba71c18805b3842c36801e0d60d609222a
This paper investigates a probabilistic speaker identification method based on the dual Penalized Logistic Regression Machines (dPLRMs). The machines employ kernel functions which map an acoustic feature space to a higher dimensional space as is the case with the Support Vector Machines (SVMs).
https://dl.acm.org/doi/10.1023/A%3A1009715923555
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail.
https://www.sciencedirect.com/science/article/pii/S0925231201006762
Modified support vector machines in financial time series forecasting. ... Other types of kernel functions will be explored for further improving the performance of SVMs in financial time series forecasting. ... Identifying speaker with support vector networks, Interface …Cited by: 525
https://link.springer.com/article/10.1007/s00521-012-1324-4
Jan 24, 2013 · Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical …
https://www.sciencedirect.com/science/article/pii/S0305048301000263
This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. The objective of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network.Cited by: 1242
http://image.diku.dk/imagecanon/material/cortes_vapnik95.pdf
SUPPORT-VECTOR NETWORKS 275 Figure 2. An example of a separabl e problem in a 2 dimensional space. The support vectors , marked with grey squares, …
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