Voice Recognition System using SVM

Support Vector Machines (SVM) are used in speech recognition systems to classify audio input in order to identify or validate a speaker based on distinctive voice features. Because it determines the best border (hyperplane) to divide classes with the greatest margin, SVM is a supervised machine learning technique that works well for classification problems. This makes it useful for differentiating between speakers or sound patterns.

Important Steps in Using SVM Data Collection to Build a Voice Recognition System: Take audio samples from various speakers or record them. Usually, this data consists of brief audio snippets with each speaker’s identification labelled.

Feature extraction is the process of turning unprocessed audio data into numerical features that represent the unique qualities of each voice.

Preprocessing and Normalisation: Divide the dataset into training and testing sets and normalise features for consistency. The SVM classifier’s ability to generalise effectively to unknown data is ensured by this phase.

Train the SVM Model: An SVM model is trained using the characteristics that have been extracted. By constructing a hyperplane that maximises the margin between classes, the SVM algorithm divides the data points of various speakers. Complex, non-linear audio data can be better classified by using kernel functions (such as linear, polynomial, or radial basis).

Model Evaluation: To assess the model’s recall, accuracy, and precision, test it on a validation set. An effective SVM model should reliably distinguish between speakers in the test set.

Real-Time or Batch Processing: For real-time applications, the system can continuously capture audio, extract features, and classify in near