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Deep belief networks for i-vector based speaker recognition
dc.contributor.author | Ghahabi Esfahani, Omid |
dc.contributor.author | Hernando Pericás, Francisco Javier |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2015-04-07T14:50:36Z |
dc.date.created | 2014 |
dc.date.issued | 2014 |
dc.identifier.citation | Ghahabi, O.; Hernando, J. Deep belief networks for i-vector based speaker recognition. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014): Florence, Italy, 4-9 May 2014". Florència: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 1700-1704. |
dc.identifier.isbn | 978-1-4799-2894-1 |
dc.identifier.uri | http://hdl.handle.net/2117/27145 |
dc.description.abstract | The use of Deep Belief Networks (DBNs) is proposed in this paper to model discriminatively target and impostor i-vectors in a speaker verification task. The authors propose to adapt the network parameters of each speaker from a background model, which will be referred to as Universal DBN (UDBN). It is also suggested to backpropagate class errors up to only one layer for few iterations before to train the network. Additionally, an impostor selection method is introduced which helps the DBN to outperform the cosine distance classifier. The evaluation is performed on the core test condition of the NIST SRE 2006 corpora, and it is shown that 10% and 8% relative improvements of EER and minDCF can be achieved, respectively. |
dc.format.extent | 5 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.other | Deep belief network |
dc.subject.other | i-vector |
dc.subject.other | Neural network |
dc.subject.other | Speaker recognition |
dc.title | Deep belief networks for i-vector based speaker recognition |
dc.type | Conference report |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.1109/ICASSP.2014.6853888 |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 15207525 |
dc.description.version | Postprint (published version) |
dc.date.lift | 10000-01-01 |
local.citation.author | Ghahabi, O.; Hernando, J. |
local.citation.contributor | IEEE International Conference on Acoustics, Speech, and Signal Processing |
local.citation.pubplace | Florència |
local.citation.publicationName | 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014): Florence, Italy, 4-9 May 2014 |
local.citation.startingPage | 1700 |
local.citation.endingPage | 1704 |