1 publication

• Published Subscription 21 Jan 2021

  Principal Component Analysis Based Dominant Features Selection Method for Speaker Identification

  By Md. Ekramul Hamid, Md. Khademul Islam Molla, Wade Ghribi

  Abstract: Cepstrum based features are mostly used in speaker identification. Mel-frequency cepstrum coefficients (MFCCs) and their statistical properties (skewness, kurtosis and standard deviation) are used in this paper for text-dependent speaker identification. Principal component analysis (PCA) is employed to select the dominant feature vector representing the speaker characteristics. Multi-layer neural network is used as the classification engine. There occurs the inter-speaker variation of speech length uttering the same word. The feature …

  Published in Current Trends in Signal Processing · Vol. 1, Issue 1-3, 2025 · pp. 33–45 Read article →