ONLINE SPEAKER DIARIZATION FOR MULTIMEDIA DATA RETRIEVAL ON MOBILE DEVICES

Cited 3 time in webofscience Cited 0 time in scopus
  • Hit : 538
  • Download : 0
Speaker diarization detects speaker change points in spoken data and organizes speaker clusters so that each cluster contains one speaker's segments. This study aims to develop online speaker diarization for multimedia data retrieval on mobile devices. Researchers have proposed various methods of diarization, but most approaches thus far depend on an empirically determined threshold as a criterion or work in an offline manner that requires prior knowledge, such as the overall number of speakers. There are therefore clear drawbacks with mobile devices, on which various types of spoken data are frequently played and replaced. A new approach to online speaker segmentation and clustering is proposed for overcoming these drawbacks. The proposed segmentation method considers the temporal locality of an analysis window, assuming that each window contains only a small number of speakers. In accordance with this property, a local universal background model (UBM) is constructed in a window and the model is used to detect speaker change points. A cluster boundary-based dynamic decision criterion is proposed for speaker clustering. This approach estimates the internal characteristics of clusters and uses them to determine cluster boundaries. In experiments using a broadcast news corpus, our techniques exhibited superior performance compared to conventional approaches.
Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
Issue Date
2012-12
Language
English
Article Type
Article
Keywords

GAUSSIAN MIXTURE-MODELS; SEGMENTATION

Citation

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, v.26, no.8

ISSN
0218-0014
DOI
10.1142/S0218001412600117
URI
http://hdl.handle.net/10203/173477
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0