Database Development and Automatic Speech Recognition of Isolated Pashto Spoken Digits Using MFCC and K-NN
Zakir Ali, Arbab Waseem Abbas, Thasleema T. M, Burhan uddin, Tanzeela
International Journal of Computational Linguistics Research, Vol. 5, No. 3, 2014.
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Abstract:
Automatic recognition of isolated spoken digits is one of the most challenging tasks in the area of automatic speech recognition. In this paper, database development and automatic speech recognition of isolated Pashto spoken
digits from sefer (0) to naha (9) has been presented. A number of 50 individual Pashto native speakers (25 male and 25 female) of different ages, ranging from 18 to 60 years, were involved to utter from sefer (0) to naha (9) digits separately. Sony PCM-M 10 linear recorder is used for recoding purpose in the office and home in noise free environment. Adobe
audition version 1.0 is used to split the audio of digits into individual digits and result is saved in .wav format. Mel Frequency Cepstral Coefficients (MFCC) is used to extract speech features. K Nearest Neighbor (K-NN) classifier is used for the first time up to author knowledge in Pashto language to classify the features of speech. The experimental results are evaluated, and the overall average recognition accuracy of 76.8% is obtained.