Speech Recognition with Clustering Algorithms

In this article, we will conduct an in-depth exploration of the k-means clustering algorithm, commonly implemented using functions like scikit-learn's KMeans() in Python or kmeans() in MATLAB. The k-means algorithm is an unsupervised clustering technique designed to partition data points into distinct clusters based on similarity metrics. The core algorithmic logic involves iterative centroid calculations to optimize data point distribution within clusters. Centroids represent the central points of each cluster, computed as the mean of all points belonging to that cluster. During each iteration, the algorithm calculates distances (typically Euclidean distance) between each data point and all centroids using distance computation methods, then assigns points to their nearest clusters. Subsequently, it recalculates centroids based on new cluster assignments and repeats this process until convergence criteria are met—either when centroid positions stabilize or when a predefined maximum iteration count is reached. Beyond k-means, numerous alternative clustering algorithms exist with distinct implementation approaches, such as hierarchical clustering (using linkage matrices and dendrograms) and DBSCAN (density-based spatial clustering with noise detection parameters). These algorithms demonstrate varying performance characteristics across different datasets, requiring appropriate algorithm selection based on specific problem domains. Furthermore, to optimize clustering algorithm applications, essential preprocessing steps like data normalization using StandardScaler and feature selection techniques (e.g., Principal Component Analysis) must be implemented to enhance algorithmic accuracy and computational efficiency through dimensionality reduction.