An embedded segmental K-means model for unsupervised segmentation and clustering of speech
An embedded segmental K-means model for unsupervised segmentation and clustering of speech
Unsupervised segmentation and clustering of unlabelled speech are core problems in zero-resource speech processing. Most approaches lie at methodological extremes: some use probabilistic Bayesian models with convergence guarantees, while others opt for more efficient heuristic techniques. Despite competitive performance in previous work, the full Bayesian approach is difficult to scale …