As,

is the feature vector speech segment used

to made model

. ?Ac?

is the cardinality of a set and ?W?

is the weighted sum of covariance matrices Cov (Ac). So, only the

partition is selected during the final clustering this result in minimum WCD

score 73.

2.5.3 Issues Related

with Algorithm

There are many other

issues need attention in speaker clustering. A uniform model for all segments from

the cluster belonging could be built called average linkage but this is quite

expensive in terms of computational costs and furthermore suggest that some

other form of linkage for segments or model in the cluster may even more

suitable-complete linkage used to compute the distance between the two cluster

for individual points.

(Sander and Ester, 2000)

single linkage on other hand use the distance of the nearest pair of each to

represent the distance of pair.

GMM

training process should be initializing with some reference point

. The expected maximization (EM)

algorithm will help to identify a local maximum likelihood regardless of the

starting point but likelihood equation for GMM has many starting point and

maxima models that give different maxima K-mean and K-mean++ are some of the initialization

employed but unfortunately maximum of them are not up to the mark and take lots

of iterations to coverage 74.

As

from above Fig. 2.5 when training a

nodal variance GMM it has been found that variance elements become small in

magnitude which is particularly true for a mixture model with a large number of

component densities (?32). Such small variances generates a singularity inside

the likelihood function of model and Detroit identification performance by

distorting speaker model score used in maximum likelihood classifier. To avoid

this problem which will cause numerical instability, a maximum variance value

on elements of all variance vectors is added in a speaker’s model.

2.6 Training of

Acoustic Model

Acoustic

model is used to collect speech feature numerical data in large quantities expressed

in term of parameters and is very important in refinement of various speech

classes and acoustic model in speech recognition build on the base of Hidden

Markov Models (HMM). Acoustic model are used in evaluating probability from

speech to an acoustic unit or an acoustic hypothesis and language model are

used to identify the probability of word sequence. Most of the recognition

systems follow HMM as the acoustic modelling rule.