scholarly journals Speaker Authentication Using a Formant‐Tracking Vocoder

1969 ◽  
Vol 46 (1A) ◽  
pp. 90-90
Author(s):  
J. R. Richards
Keyword(s):  
Formant Tracking ◽  
Speaker Authentication

Recent advancements in automatic speaker authentication

1999 ◽  
Vol 6 (1) ◽  
pp. 24-34 ◽  
Author(s):  
Qi Li ◽  
Biing-Hwang Juang ◽  
Chin-Hui Lee ◽  
Qiru Zhou ◽  
F.K. Soong
Keyword(s):  
Speaker Authentication

Differentially Fed Artificial Neural Networks for Speech Signal Prediction

2011 ◽  
pp. 309-323
Author(s):  
Manjunath Ramachandra Iyer
Keyword(s):  
Personal Identification ◽  
Final Decision ◽  
Personal Identification Number ◽  
Limited Data ◽  
Data Set ◽  
New Class ◽  
Signal Prediction ◽  
Speaker Authentication ◽  
Artificial Neural ◽  
Security Checks

Speaker authentication has become increasingly important. It goes with the other forms of security checks such as user login and personal identification number and has a say in the final decision about the authenticity. One of the issues with the authentication algorithms is that the automated devices that take the call have to work with a limited data set. In this chapter, a new class of intelligent element called differentially fed artificial neural network has been introduced to predict the data and use it effectively. It keeps the model simple and helps in taking online and crisp decisions with the available limited data.

Fuzzy Clustering-Based Approaches in Automatic Lip Segmentation from Color Images

2006 ◽  
pp. 292-317
Author(s):  
Shilin Wang ◽  
Wing Hong Lau ◽  
Alan Wee-Chung Liew ◽  
Shu Hung Leung
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Visual Information ◽  
Spatial Information ◽  
Noisy Environments ◽  
Low Contrast ◽  
Speaker Authentication ◽  
Lip Segmentation ◽  
Traditional Image

Recently, lip image analysis has received much attention because the visual information extracted has been shown to provide significant improvement for speech recognition and speaker authentication, especially in noisy environments. Lip image segmentation plays an important role in lip image analysis. This chapter will describe different lip image segmentation techniques, with emphasis on segmenting color lip images. In addition to providing a review of different approaches, we will describe in detail the state-of-the-art classification-based techniques recently proposed by our group for color lip segmentation: “Spatial fuzzy c-mean clustering” (SFCM) and “fuzzy c-means with shape function” (FCMS). These methods integrate the color information along with different kinds of spatial information into a fuzzy clustering structure and demonstrate superiority in segmenting color lip images with natural low contrast in comparison with many traditional image segmentation techniques.

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