Impact of SVM-based Poisoning on the Semantic Recognition of Sounds

Abstract. Machine learning is a technique that enables computers to learn from data and make predictions or decisions, data poisoning is the process of machine learning training where malicious samples are put in to make the model predictions or classifications less accurate. Data poisoning attacks help to reveal security vulnerabilities in AI systems. In this paper, we study Support Vector Machine (SVM) poisoning for sound recognition techniques, using AISHELL-3 dataset training data, from which we find the most vulnerable features for SVM poisoning. In the field of speech recognition, SVM can be applied to speech feature extraction, speech classification and speech synthesis to find the best hyperplane by finding the maximum margin optimization for effective classification and recognition of speech signals. Experiments have resulted in biased semantic recognition of sounds, output of incorrect speech, reduced accuracy of model classification and generation of incorrect decision boundaries. The role of this research paper is to investigate whether SVM poisoning affects the semantic recognition of sounds, and the result of the research is that it does cause semantic bias.