A Riemannian Stopping Criterion for Unsupervised Phonetic Segmentation

With the availability of large and heterogeneous corpora of untranscribed speech we have recently seen regained interest for algorithms to perform automatic segmentation of such data into acoustically homogeneous or phonetic units. In this paper, we face the problem of phonetic segmentation under a hierarchical clustering (HC) framework. Concretely, we focus on the task of automatically estimating the optimum number of segments in speech data. For this purpose we present aRiemannian stopping criterion that is able to automatically stop the HC processing when it is close to the underlying number of phonetic segments while providing a lower variance(robust) estimation of the optimal number of segments. We test the proposed criterion using TIMIT data and show that it outperforms previous approaches obtaining a significantly lower over/under-segmentation variance by 46, 1% relative and average improvement of 0:14 compared to a previously proposed approach. We also show that the proposed method is robust in automatically finding the correct number of segments under data source variations.