The 2016 speakers in thewild speaker recognition evaluation

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The newly collected Speakers in the Wild (SITW) database was central to a text-independent speaker recognition challenge held as part of a special session at Interspeech 2016. The SITW database is composed of audio recordings from 299 speakers collected from open source media, with an average of 8 s...

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Autor principal: McLaren, M.
Otros Autores: Ferrer, L., Castan, D., Lawson, A., Morgan N., Georgiou P., Narayanan S., Metze F., Amazon Alexa; Apple; eBay; et al.; Google; Microsoft
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: International Speech and Communication Association 2016
Acceso en línea:Registro en Scopus
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Biblioteca Central Dr. Luis F. Leloir (FCEN) de Universidad de Buenos Aires
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100 1 |a McLaren, M. 
245 1 4 |a The 2016 speakers in thewild speaker recognition evaluation 
260 |b International Speech and Communication Association  |c 2016 
506 |2 openaire  |e Política editorial 
504 |a NIST Speaker Recognition Evaluations, , http://www.nist.gov/itl/iad/mig/sre.cfm 
504 |a Gonzalez-Rodriguez, J., Evaluating automatic speaker recognition systems: An overview of the nist speaker recognition evaluations (1996-2014) (2014) Loquens, 1 (1) 
504 |a McLaren, M., Ferrer, L., Castan, D., Lawson, A., The speakers in the wild (SITW) speaker recognition database (2016) Submitted to Interspeech, 2016 
504 |a Poh, N., Bengio, S., Estimating the confidence interval of expected performance curve in biometric authentication using joint bootstrap (2007) Proc. ICASSP, Honolulu, , Apr 
504 |a Lei, Y., Scheffer, N., Ferrer, L., McLaren, M., A novel scheme for speaker recognition using a phonetically-aware deep neural network (2014) Proc. ICASSP, Florence, Italy, , May 
504 |a Matejka, P., Zhang, L., Ng, T., Mallidi, S.H., Glembek, O., Ma, J., Zhang, B., Neural network bottleneck features for language identification (2014) Proc. Odyssey-14, Joensuu, Finland, , Jun 
504 |a McLaren, M., Lei, Y., Ferrer, L., Advances in deep neural network approaches to speaker recognition (2015) Proc. ICASSP, , Brisbane, Australia, May 
504 |a Mclaren, M., Van Leeuwen, D., Source-normalized lda for robust speaker recognition using i-vectors from multiple speech sources (2012) Audio, Speech, and Language Processing, IEEE Transactions on, 20 (3), pp. 755-766 
504 |a Zhou, X., Garcia-Romero, D., Duraiswami, R., Espy-Wilson, C., Shamma, S., Linear versus mel frequency cepstral coefficients for speaker recognition (2011) Automatic Speech Recognition and Understanding (ASRU 2011 IEEE Workshop on, , IEEE 
504 |a Garcia-Romero, D., Espy-Wilson, C., Analysis of i-vector length normalization in speaker recognition systems (2011) Proc. Interspeech, , Florence, Italy, Aug 
504 |a Ferrer, L., McLaren, M., Scheffer, N., Lei, Y., Graciarena, M., Mitra, V., A noise-robust system for NIST 2012 speaker recognition evaluation (2013) Proc. Interspeech, , Lyon, France, Aug 
504 |a DARPA RATS Program, , http://www.darpa.mil/program/robust-atuomatic-transcription-of-speech 
504 |a Thomas, S., Saon, G., Van Segbroeck, M., Narayanan, S.S., Improvements to the IBM speech activity detection system for the DARPA rats program (2015) Proc. ICASSP, , Brisbane, Australia, May 
504 |a Ma, J., Improving the speech activity detection for the DARPA RATS phase-3 evaluation (2014) Proc. Interspeech, , Singapore, Sep 
504 |a Graciarena, M., Alwan, A., Ellis, D., Franco, H., Ferrer, L., Hansen, J.H., Janin, A., Mitra, V., All for one: Feature combination for highly channel-degraded speech activity detection (2013) Proc. Interspeech, , Lyon, France, Aug 
504 |a Ferrer, L., Graciarena, M., Mitra, V., A phonetically aware system for speech activity detection Proc. ICASSP, , Shanghai, China, March 2016 
504 |a Ferrer, L., Burget, L., Plchot, O., Scheffer, N., A unified approach for audio characterization and its application to speaker recognition (2012) Proc. Odyssey-12, , Singapore, Jun 
504 |a McLaren, M., Lawson, A., Ferrer, L., Scheffer, N., Lei, Y., Trial-based calibration for speaker recognition in unseen conditions (2014) Proc. Odyssey-14, , Joensuu, Finland, JunA4 - Amazon Alexa; Apple; eBay; et al.; Google; Microsoft 
520 3 |a The newly collected Speakers in the Wild (SITW) database was central to a text-independent speaker recognition challenge held as part of a special session at Interspeech 2016. The SITW database is composed of audio recordings from 299 speakers collected from open source media, with an average of 8 sessions per speaker. The recordings contain unconstrained or "wild" acoustic conditions, rarely found in large speaker recognition datasets, and multi-speaker recordings for both speaker enrollment and verification. This article provides details of the SITW speaker recognition challenge and analysis of evaluation results. There were 25 international teams involved in the challenge of which 11 teams participated in an evaluation track. Teams were tasked with applying existing and novel speaker recognition algorithms to the challenges associated with the real world conditions of SITW. We provide an analysis of some of the top performing systems submitted during the evaluation and provide future research directions. Copyright ©2016 ISCA.  |l eng 
593 |a Speech Technology and Research Laboratory, SRI InternationalCA, United States 
593 |a Departamento de Computación, FCEN, Universidad de Buenos Aires, CONICET, Argentina 
690 1 0 |a EVALUATION 
690 1 0 |a SPEAKER RECOGNITION 
690 1 0 |a SPEAKERS IN THE WILD DATABASE 
690 1 0 |a AUDIO RECORDINGS 
690 1 0 |a CHARACTER RECOGNITION 
690 1 0 |a DATABASE SYSTEMS 
690 1 0 |a SPEECH COMMUNICATION 
690 1 0 |a SPEECH PROCESSING 
690 1 0 |a ACOUSTIC CONDITIONS 
690 1 0 |a EVALUATION 
690 1 0 |a EVALUATION RESULTS 
690 1 0 |a FUTURE RESEARCH DIRECTIONS 
690 1 0 |a INTERNATIONAL TEAM 
690 1 0 |a SPEAKER RECOGNITION 
690 1 0 |a SPEAKER RECOGNITION EVALUATIONS 
690 1 0 |a TEXT INDEPENDENTS 
690 1 0 |a SPEECH RECOGNITION 
700 1 |a Ferrer, L. 
700 1 |a Castan, D. 
700 1 |a Lawson, A. 
700 1 |a Morgan N. 
700 1 |a Georgiou P. 
700 1 |a Morgan N. 
700 1 |a Narayanan S. 
700 1 |a Metze F. 
700 1 |a Amazon Alexa; Apple; eBay; et al.; Google; Microsoft 
711 2 |d 8 September 2016 through 16 September 2016  |g Código de la conferencia: 124342 
773 0 |d International Speech and Communication Association, 2016  |g v. 08-12-September-2016  |h pp. 823-827  |p Proc. Annu. Conf. Int. Speech. Commun. Assoc., INTERSPEECH  |n Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH  |x 2308457X  |t 17th Annual Conference of the International Speech Communication Association, INTERSPEECH 2016 
856 4 1 |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994304123&doi=10.21437%2fInterspeech.2016-1137&partnerID=40&md5=4c675e27f1748ad61fbc4a5ae42d195f  |y Registro en Scopus 
856 4 0 |u https://doi.org/10.21437/Interspeech.2016-1137  |y DOI 
856 4 0 |u https://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p823_McLaren  |y Handle 
856 4 0 |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2308457X_v08-12-September-2016_n_p823_McLaren  |y Registro en la Biblioteca Digital 
961 |a paper_2308457X_v08-12-September-2016_n_p823_McLaren  |b paper  |c PE 
962 |a info:eu-repo/semantics/conferenceObject  |a info:ar-repo/semantics/documento de conferencia  |b info:eu-repo/semantics/publishedVersion 
999 |c 77392 

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