Earticle

초록

영어

This paper presents a new Speaker Recognition Technique aimed at high identification accuracy and low impostor acceptance. This method is based on a modified neural network, which is an extended and improved version of a Self-Organizing Map in multiple dimensions. The goal of this methodology is to achieve high accuracy identification and impostor rejection. The proposed method, Multiple Parametric Self-Organizing Maps (M-PSOM) is a classification and verification technique. This novel method was successfully implemented and tested using the CSLU Speaker Recognition Corpora of the Oregon School of Engineering with excellent results. This method builds a unique parametric neural network for each speaker as opposed to a single neural network for the whole system as it has been done in the past. With this technology a parametric neural network is a unique representation of a speaker’s acoustic signature.

목차

Abstract
 1. Introduction
 2. The Multiple Parametric Self-Organizing Maps (M-PSOM)
 3. The Parametric SOM (PSOM) Model
 4. The MPSOM Training Algorithm
 5. The MPSOM Architecture
 6. The Multiple PSOM Retrieval Algorithm
 7. The Distortion Computation Algorithm
 8. Experiment Methodology and Data Selection
 9. Results and Analysis
 10. Conclusions
 References

키워드

저자

• Paul Gomez [ Quantum Nanoelectronics, Inc., R&D Department, 12851 Luray Rd, SW Ranches, FL ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605