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Mohamad Khairul Najmi Zailan Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia Yusnita Mohd Ali Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia Emilia Noorsal Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia Mohd Hanapiah Abdullah Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia Zuraidi Saad Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia Adni Mat Leh Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia |
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| Abstract | |
| Speech is the utmost communication medium for human beings which conveys rich and valuable information such as accent, gender, emotion and unique identity. Therefore, automatic speaker recognition can be developed based on unique characteristics of one’s speech and utilized for applications such as voice dialing, online banking, and telephone shopping to verify the identity of its users. However, retrieving salient features which are capable of identifying speakers is a challenging problem in speech recognition systems since speech contains abundant information. In this study, a total of 438 audio data obtained from speakers uttering speech in text-independent context is proposed using speech data elicited from three Malay male speakers. The performance of two popularly used feature extraction techniques namely, linear prediction coefficients (LPC) and Mel-frequency cepstral coefficients (MFCC) were compared using discriminant analysis model. Although both features yielded impressive outcomes, the MFCC features surpassed that of LPC by achieving a higher accuracy rate of 99.09%, which was 4.34% higher than the latter. |
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Keyword: speaker recognition; biometric; linear prediction coefficients; mel-frequency coefficients; discriminant analysis |
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Comparative Analysis of LPC and MFCC for Male Speaker Recognition in Text-Independent Context
