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Classification of Acoustic Data Using the FF Neural Network and Random Forest Method

According to their acoustic analysis, speaker identification systems are intended to identify the speaker or group of speakers. Many methods are used to conduct acoustic analysis on a speech signal, with time and frequency domain analysis being the most common. The MFCC and Fundamental Frequency techniques are used in this article to extract acoustic information from speech samples. Two distinct methods, Random-forest and Feed Forward Neural Network, are used to classify the findings. The combination of the FFNN classifier with the acoustic model yielded a recognition accuracy of 91.4 percent. This paper makes use of the CMU ARCTIC Database.