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Support Vector Machines (SVMs) are a powerful machine learning algorithm widely used in speech recognition. SVMs work by finding the best hyperplane to separate different groups of audio data. The advantages of SVMs include the ability to handle non-linear data, high accuracy, and good generalization. The process of using SVMs involves extracting features from audio signals, building an SVM model, training, and evaluating the model. SVMs have been applied in various fields such as speaker recognition, voice-controlled systems, and virtual assistants. However, the performance of SVMs depends on data quality, feature selection, and model parameters.