Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework

Algorithm 2

PNN learning algorithm.

(1) Statement: Classify input vowel patterns 𝑋 into

two category of vowel, VOWEL-A and VOWEL-B

(2) Initialize: Smoothing parameter 𝜎=0.111

(Determined from observation of successful learning)

(3) Output of each pattern unit,

𝑍_𝑖=𝑋⋅𝑊_𝑖

(𝑊_𝑖 is the weight vector)

(4) Find neuron activation function by performing non-linear operation of the form

            𝑔(𝑍_𝑖)=exp[(𝑍_𝑖−1)/𝜎²]

(5) Sum all the 𝑔(𝑍_𝑖) for category VOWEL-A and do the same for category VOWEL-B

(6) Take binary decision for the two summation outputs with variable weight given by-

            𝐶_𝑘=−(ℎ_𝐵𝐼_𝐵/ℎ_𝐴𝐼_𝐴)(𝑛_𝐴/𝑛_𝐵)

where,

ℎ_𝐴 and ℎ_𝐵= Priori probability of occurrence of pattern from VOWEL-A and VOWEL-B respectively

  𝐼= Loss associated with wrong decision

𝑛_𝐴 and 𝑛_𝐵= No of patterns in

VOWEL-A and VOWEL-B respectively which is 10 for both categories