Text-constrained speaker verification using fuzzy C means vector quantization

2015 ◽  
Author(s):  
Debnath Saswati ◽  
Soni Badal ◽  
Das Pradip K.
Keyword(s):  
Vector Quantization ◽  
Speaker Verification ◽  
Fuzzy C Means

scholarly journals Tutorial sobre Fuzzy-c-Means e Fuzzy Learning Vector Quantization: Abordagens Híbridas para Tarefas de Agrupamento e Classificação

2012 ◽  
Vol 19 (1) ◽  
pp. 120 ◽  
Author(s):  
Sarajane Marques Peres ◽  
Thiago Rocha ◽  
Helton H. Biscaro ◽  
Renata Cristina B. Madeo ◽  
Clodis Boscarioli
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Fuzzy C Means ◽  
Fuzzy Learning

Noise Fuzzy Learning Vector Quantization

2010 ◽  
Vol 439-440 ◽  
pp. 367-371
Author(s):  
Xiao Hong Wu ◽  
Bin Wu ◽  
Jie Wen Zhao
Keyword(s):  
Vector Quantization ◽  
Noisy Data ◽  
Learning Vector Quantization ◽  
Experimental Results ◽  
Fuzzy C Means ◽  
Learning Rates ◽  
Sensitivity Problem ◽  
Noise Clustering ◽  
Fuzzy Learning ◽  
Better Than

Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises). Here, a new fuzzy learning vector quantization model, called noise fuzzy learning vector quantization (NFLVQ), is proposed to handle the noises sensitivity problem of FLVQ. NFLVQ integrates LVQ and generalized noise clustering (GNC), uses the membership values from GNC as learning rates and clusters data containing noisy data better than FLVQ. Experimental results show the better performances of NFLVQ.

Sign in / Sign up

Export Citation Format

Share Document