<p>Speaking one’s language is the most common and efficient way to convey ideas and information. People can express themselves verbally by putting their ideas, emotions, and experiences into words. However, with the rapid advancements in technology, criminals often use voice changers as anti-forensic tools to conceal their voices while committing illegal activities. In this proposed work, normal voice (NV) is disguised using two methods: objects in the mouth (OM) and whispering (WP), which are considered for experimental purposes. Features are extracted using Mel Frequency Cepstral Coefficients (MFCC) and their derivatives, as well as Tonal Frequency Cepstral Coefficients (TFCC) and their derivatives, for both normal and disguised voices. After feature extraction, several machine learning classifiers are employed to differentiate between disguised and normal voices. Based on above abstract write a suitable paper title. The classifiers used include Support Vector Machine (SVM), Decision Tree (DT), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Logistic Regression (LR). For speaker identification from WP-disguised voices, the proposed method achieved accuracies of 82.50%, 95.67%, 83.24%, 88.52%, and 80.64% using SVM, DT, LDA, LR, and NB respectively. For OM-disguised voices, the achieved accuracies were 85.62%, 83.90%, 84.60%, 52.67%, and 83.32% for SVM, DT, LDA, LR, and NB respectively. The comparison between the proposed and existing methods demonstrates that the proposed system achieves better classification performance. In the future, the system can be enhanced for real-world applications by expanding the dataset, incorporating additional physical disguise methods, and employing hybrid feature extraction techniques.</p>