title: Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering.
abstract: Agglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments. The algorithm is based on an iterative divide-and-conquer strategy. The data is first split into independent subsets, each of which is clustered separately. Thus reduces the storage required for sequential implementations, and allows concurrent computation on parallel computing hardware. The resultant clusters are merged and subsequently re-divided into subsets, which are passed to the following iteration. We show that MAHC can match and even surpass the performance of the exact implementation when applied to datasets of speech segments.
authors.affiliation: Department of Electrical and Electronic Engineering, University of Stellenbosch, Western Cape, South Africa.
authors.forename: Lerato
authors.initials: L
authors.lastname: Lerato
authors.affiliation: Department of Electrical and Electronic Engineering, University of Stellenbosch, Western Cape, South Africa.
authors.forename: Thomas
authors.initials: T
authors.lastname: Niesler
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D066264:Datasets as Topic; D013061:Speech Acoustics