@Article{10798587.2017.1278961,
AUTHOR = {Mohammed Algabri, Mohamed Abdelkader Bencherif, Mansour Alsulaiman, Ghulam Muhammad, Mohamed Amine Mekhtiche},
TITLE = {Soft Computing Techniques for Classification of Voiced/Unvoiced Phonemes},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {267--274},
URL = {http://www.techscience.com/iasc/v24n2/39753},
ISSN = {2326-005X},
ABSTRACT = {A method that uses fuzzy logic to classify two simple speech features for the automatic classification 
of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computing 
techniques are used to enhance the performance of fuzzy logic by tuning the parameters of the 
membership functions, are also presented. The three methods, manually constructed fuzzy logic 
(VUFL), fuzzy logic optimized with genetic algorithm (VUFL-GA), and fuzzy logic with optimized 
particle swarm optimization (VUFL-PSO), are implemented and then evaluated using the TIMIT speech 
corpus. Performance is evaluated using the TIMIT database in both clean and noisy environments. Four 
different noise types from the AURORA database—babble, white, restaurant, and car noise—at six 
different signal-to-noise ratios (SNRs) are used. In all cases, the optimized fuzzy logic methods (VUFLGA and VUFL-PSO) outperformed manual fuzzy logic (VUFL). The proposed method and variants are 
suitable for applications featuring the presence of highly noisy environments. In addition, classification 
accuracy by gender is also studied.},
DOI = {10.1080/10798587.2017.1278961}
}