An unsupervised scheme for speaker indexing of audio databases

Author

Chen, Yanxiang ; Liu, Ming

Author_Institution

Coll. of Comput. Sci. & Inf., Hefei Univ. of Technol., Hefei, China

Volume

3

fYear

2009

fDate

20-22 Nov. 2009

Firstpage

90

Lastpage

93

Abstract

Speaker indexing of an audio database consists in organizing the audio data according to the speakers present in the database. This paper investigates on segmenting and clustering continuous audio streams automatically by speaker with no prior speaker model. It is composed of two steps: (1) segmentation based on GLR distance measure and BIC refinement, (2) clustering based on agglomerative clustering and pruning selection. The aim is to produce just one pure cluster for every speaker. Results are presented using the data sets derived from the Switchboard corpus and the effectiveness of the proposed scheme is shown.

Keywords

audio databases; pattern clustering; unsupervised learning; BIC refinement; Bayesian information criterion; GLR distance measurement; Switchboard corpus; agglomerative clustering; audio databases; audio streams clustering; audio streams segmentation; generalized likelihood ratio; pruning selection; speaker indexing; unsupervised indexing scheme; Audio databases; Computer science; Data engineering; Educational institutions; Indexing; Merging; Organizing; Speech; Streaming media; Time measurement; BIC refinement; GLR distance measure; pruning selection; speaker indexing;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5358240

Filename

5358240