Multiple Acoustic Model-Based Discriminative Likelihood Ratio Weighting for Voice Activity Detection

Cited 21 time in webofscience Cited 0 time in scopus
  • Hit : 497
  • Download : 0
In this letter, we propose a novel statistical voice activity detection (VAD) technique. The proposed technique employs probabilistically derived multiple acoustic models to effectively optimize the weights on frequency domain likelihood ratios with the discriminative training approach for more accurate voice activity detection. Experiments performed on various AURORA noisy environments showed that the proposed approach produces meaningful performance improvements over the single acoustic model-based conventional approaches.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2012-08
Language
English
Article Type
Article
Citation

IEEE SIGNAL PROCESSING LETTERS, v.19, no.8, pp.507 - 510

ISSN
1070-9908
DOI
10.1109/LSP.2012.2204978
URI
http://hdl.handle.net/10203/103036
Appears in Collection
EE-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 21 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0