Non-Gaussian density estimation for the classification of acoustic feature vectors in speech recognition

2002 ◽  
Vol 111 (3) ◽  
pp. 1155
Author(s):  
Sankar Basu ◽  
Charles A. Micchelli
2020 ◽  
Vol 8 (1) ◽  
pp. 45-69
Author(s):  
Eckhard Liebscher ◽  
Wolf-Dieter Richter

AbstractWe prove and describe in great detail a general method for constructing a wide range of multivariate probability density functions. We introduce probabilistic models for a large variety of clouds of multivariate data points. In the present paper, the focus is on star-shaped distributions of an arbitrary dimension, where in case of spherical distributions dependence is modeled by a non-Gaussian density generating function.


2014 ◽  
Vol 23 (5) ◽  
pp. 749-795 ◽  
Author(s):  
MIREILLE BOUSQUET-MÉLOU ◽  
KERSTIN WELLER

Let${\cal A}$be a minor-closed class of labelled graphs, and let${\cal G}_{n}$be a random graph sampled uniformly from the set ofn-vertex graphs of${\cal A}$. Whennis large, what is the probability that${\cal G}_{n}$is connected? How many components does it have? How large is its biggest component? Thanks to the work of McDiarmid and his collaborators, these questions are now solved when all excluded minors are 2-connected.Using exact enumeration, we study a collection of classes${\cal A}$excluding non-2-connected minors, and show that their asymptotic behaviour may be rather different from the 2-connected case. This behaviour largely depends on the nature of the dominant singularity of the generating functionC(z) that counts connected graphs of${\cal A}$. We classify our examples accordingly, thus taking a first step towards a classification of minor-closed classes of graphs. Furthermore, we investigate a parameter that has not received any attention in this context yet: the size of the root component. It follows non-Gaussian limit laws (Beta and Gamma), and clearly merits a systematic investigation.


2020 ◽  
Vol 8 (5) ◽  
pp. 3978-3983

Identification of a person’s speech by his lip movement is a challenging task. Even though many software tools available for recognition of speech to text and vice versa, some of the words uttered may not be accurate as spoken and may vary from person to person because of their pronunciation. In addition, in the noisy environment speech uttered may not perceive effectively hence there lip movement for a given speech varies. Lip reading has added advantages when it augmented with speech recognition, thus increasing the perceived information. In this paper, the video file of a individual person are converted to frames and extraction of only the lip contour for vowels is done by calculating its area and other geometrical aspects. Once this is done as a part of testing it is compared with three to four people’s lip contour for vowels for first 20 frames. The parameters such as mean, centroid will remain approximately same for all people irrespective of their lip movement but there is change in major and minor axis and hence area changes considerably. In audio domain vowel detection is carried out by extracting unique features of English vowel utterance using Mel Frequency Cepstrum Coefficients (MFCC) and the feature vectors that are orthonormalized to compare the normalized vectors with standard database and results are obtained with approximation.


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