THOUSEEF SYED

The main goal here was to match a voice sample from an unknown speaker to one of several labeled speaker models since speech is easily produced. For the feature extraction, Mel Frequency Cepstrum Coefficients was used since it is one of the most common features used for speaker recognition. Before extracting the features, we performed pre-processing such as Voice Activity detection to ignore unvoiced parts of the speech. For classification and objective comparison, K-Nearest Neighborhood (KNN), Convolutional Neural Network (CNN) and I-vectors/PLDA results was shared. The dataset used for the project is FEARLESS STEPS that consists of 10 hours of digitized recordings of the Apollo 11 Space Mission. These recordings were digitized by the Centre of Robust Speech Systems (CRSS) of The University of Texas at Dallas. It was typically used for speech activity detection, sentiment analysis and speaker recognition. In the research, there were a few challenges that were met using methods. Our..

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