Analysis of Feature Extraction Techniques for Speech Recognition System
Rajeev Ranjan1, Abhishek Thakur2
1Rajeev Ranjan, Department of Electronics & Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, India.
2Abhishek Thakur, Department of Electronics & Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, India.
Manuscript received on 15 May 2019 | Revised Manuscript received on 22 May 2019 | Manuscript Published on 10 July 2019 | PP: 197-200 | Volume-8 Issue-7C2 May 2019 | Retrieval Number: G10460587C219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The audio signal is filtered using a method known as feature extraction technique. In this article, the feature extraction technique for speech recognition and voice classification is analyzed and also centered to comparative analysis of different type of mel-frequency cepstral coefficients (MFCC) feature extraction method. The MFCC technique is used for deduction of noise in voice signal and also used for voice classification and speaker identification. The statistical results of the different MFCC techniques are discussed and finally concluded that the delta-delta MFCC feature extraction technique is better than the other feature extraction techniques.
Keywords: Feature Extraction; Voice Data; MFCC; Delta-Delta MFCC; Cepstral Coefficient
Scope of the Article: Communication