Using Maximization Entropy in Developing a Filipino Phonetically Balanced Wordlist for a Phoneme-level Speech Recognition System

John Lorenzo Bautista, Yoon-Joong Kim
2014 Proceedings of The 2nd International Conference on Intelligent Systems and Image Processing 2014   unpublished
In this paper, a set of Filipino Phonetically Balanced Word list consisting of 250 words (FPBW250) were constructed for a phoneme-level automatic speech recognition system for the Filipino language. The Entropy Maximization Formula is used to obtain balance phonological balance in the list. Entropy of phonemes in a word is maximized, providing an optimal balance in each word"s phonological distribution using the Add-Delete Method (PBW Algorithm) and is compared to the modified PBW Algorithm
more » ... d PBW Algorithm implemented in a dynamic algorithm approach to obtain optimization. The Filipino PBW list was extracted from 4,000 3-syllable words out of a 12,000 word dictionary and gained an entropy score of 4.2791 for the PBW Algorithm and 4.2902 for the modified algorithm. The PBW250 was recorded by 20 male and 20 female respondents, each with 2 sets data. Recordings from 30 respondents (15 male, 15 female) were trained to produce an acoustic model using a Phoneme-Based Hidden Markov Model (HMM) that were tested using recordings from 10 respondents (5 male, 5 female) using the HMM Toolkit (HTK). The results of test gave the maximum accuracy rate of 97.77% for a speaker dependent test and 89.36% for a speaker independent test.
doi:10.12792/icisip2014.029 fatcat:bwez4k52gfginjxnxj5hheqcx4