Automatic speaker recognition (ASR) is one type of biometric recognition of human, known as voice biometric recognition. Among plenty of acoustic features, Mel-Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GFCCs) are used popularly in ASR. The state-of-the-art techniques for modeling/classification(s) are Vector Quantization (VQ), Gaussian Mixture Models (GMMs), Hidden Markov Model (HMM), Artificial Neural Network (ANN), Deep Neural Network (DNN). In this paper, we cite our experimental results upon three databases, namely Hyke-2011, ELSDSR, and IITG-MV SR Phase-I, based on MFCCs and VQ/GMM where maximum log-likelihood (MLL) scoring technique is used for the recognition of speakers and analyzed the effect of Gaussian components as well as Mel-scale filter bank’s minimum frequency. By adjusting proper Gaussian components and minimum frequency, the accuracies have been increased by 10–20% in noisy environment.