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Comparative analysis of LPC and MFCC for male speaker recognition in text-independent context
Mohamad Khairul Najmi Zailan1, Yusnita Mohd Ali2, Emilia Noorsal3, Mohd Hanapiah Abdullah4, Zuraidi Saad5, Adni Mat Leh6.
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.
Affiliation:
- Universiti Teknologi MARA Cawangan Pulau Pinang, Kampus Permatang Pauh, Malaysia
- Universiti Teknologi MARA Cawangan Pulau Pinang, Kampus Permatang Pauh, Malaysia
- Universiti Teknologi MARA Cawangan Pulau Pinang, Kampus Permatang Pauh, Malaysia
- Universiti Teknologi MARA Cawangan Pulau Pinang, Kampus Permatang Pauh, Malaysia
- Universiti Teknologi MARA Cawangan Pulau Pinang, Kampus Permatang Pauh, Malaysia
- Universiti Teknologi MARA Cawangan Pulau Pinang, Kampus Permatang Pauh, Malaysia
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