Two-Level Clustering towards Unsupervised Discovery of Acoustic Classes

In this paper we focus on unsupervised discovering of acoustic classes suitable for use in pattern recognition applications. Our approach is based on a two-level clustering of an initial acoustic segmentation of the audio data in order to allow for discovery and correct modeling of complex acoustic classes. Initially, in a first-level, the acoustic space is densely clustered in order to provide a first layer of acoustic variance reduction. In a second-level clustering we use the acoustic segmentation to infer a smaller number of super-clusters taking advantage of the intra-segment relationships between the first-level clusters. In this paper we compare three possible clustering methods to obtain super-clusters as sub-sets or linear combinations of first-level clusters. Results indicate that the proposed two-level approach improves the balance between Purity and inverse Purity evaluation measures while significantly improving the stability of the transcriptions obtained when using the resulting models to transcribe the same acoustic events in different spoken utterances.