Implementing Feature Clustering with MATLAB

In the field of speech signal processing, feature clustering represents a crucial task that can be effectively implemented using tools like MATLAB. This process involves employing various clustering algorithms to group similar features together, facilitating better understanding and processing of speech signals. Through clustering analysis, we can identify patterns and trends within speech signals, establishing a solid foundation for further analysis and applications. MATLAB provides several built-in functions for clustering implementation, including k-means (kmeans function), hierarchical clustering (clusterdata function), and Gaussian mixture models (fitgmdist function). These algorithms operate by measuring feature similarity through distance metrics like Euclidean distance or cosine similarity, then iteratively optimizing cluster assignments. For speech applications, typical features might include MFCCs (Mel-frequency cepstral coefficients) or spectral centroids, which can be extracted using MATLAB's audio processing toolbox before being fed into clustering algorithms. The clustering results enable researchers to categorize different phonemes, identify speaker characteristics, or detect audio patterns for speech recognition systems.