Multiple Instance Learning for Automatic Content-Based Classification of Speech Audio

Abstract

Speech analytics researchers are working to improve their ability to decipher audio material. This research presents a new method for classifying news audio clips based on their content, called the Multiple Instance Learning (MIL) approach. Audio classification and segmentation benefit from content-based analysis. As a starting point, a classifier that can predict the category of an audio sample has been proposed. Perceptual Linear Prediction (PLP) coefficients and Mel-Frequency Cepstral Coefficients (MFCC) are two kinds of features used for audio content identification (MFCC). For classification, two MIL approaches, mi-Graph and mi-SVM, are used. Different performance matrices are used to assess the outcomes gained via the use of various approaches. The results of the experiments clearly show that the MIL has great audio categorization capacity.