Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
The application of support vector machine for speech classification |
| Συγγραφείς: |
Gauci, Oliver, Debono, Carl James, Gatt, Edward, Micallef, Paul, 4th Computer Science Annual Workshop (CSAW’06) |
| Στοιχεία εκδότη: |
University of Malta. Faculty of ICT |
| Έτος έκδοσης: |
2006 |
| Συλλογή: |
University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: |
Support vector machines, Speech processing systems -- Computer programs, Machine learning, Classification -- Software |
| Περιγραφή: |
For the classical statistical classification algorithms the probability distribution models are known. However, in many real life applications, such as speech recognition, there is not enough information about the probability distribution function. This is a very common scenario and poses a very serious restriction in classification. Support Vector Machines (SVMs) can help in such situations because they are distribution free algorithms that originated from statistical learning theory and Structural Risk Minimization (SRM). In the most basic approach SVMs use linearly separating Hyperplanes to create classification with maximal margins. However in application, the classification problem requires a constrained nonlinear approach to be taken during the learning stages, and a quadratic problem has to be solved. For the case where the classes cannot be linearly separable due to overlap, the SVM algorithm will transform the original input space into a higher dimensional feature space, where the new features are potentially linearly separable. In this paper we present a study on the performance of these classifiers when applied to speech classification and provide computational results on phonemes from the TIMIT database. ; peer-reviewed |
| Τύπος εγγράφου: |
conference object |
| Γλώσσα: |
English |
| Relation: |
https://www.um.edu.mt/library/oar//handle/123456789/22573 |
| Διαθεσιμότητα: |
https://www.um.edu.mt/library/oar//handle/123456789/22573 |
| Rights: |
info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Αριθμός Καταχώρησης: |
edsbas.92B95538 |
| Βάση Δεδομένων: |
BASE |