Proceedings Fonetik 2009 - Institutionen för lingvistik

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Proceedings, FONETIK 2009, Dept. of Linguistics, Stockholm UniversityLLR Score Test 1 Speaker JALLR Score Test 1 Imposter Speaker HE0,40,40,20,2LLR0-0,2-0,4M M M M M M F F M M F F MJA TL AE JL PN RA JV EB HV JaL KC UV SSLLR0-0,2-0,4F F F M M F M M M M M M MEB KC UV PN HV JV JA JL JaL RA SS TL AE-0,6JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t-0,6HEimHEimHEimHEimHEimHEimHEimHEimHEimHEimHEimHEimHEim-0,8-0,8-1-1Test SpeakerTest SpeakerFigure 2. Result for test 1 speaker JA against all enrolledmodels.In the 2 nd test there is a lower acceptance score(0.25) for the correct model. However, the closestmodel (TL) also has a positive LLR (0.13).LLR0,20-0,2-0,4-0,6LLR Score Test 1 Speaker HVM M M M M F M F M M F F MHV AE SS PN JaL UV RA KC JA JL JV EB TLHV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_tFigure 5. Result for test 1 female imposter speakerHE against all enrolled models.The female imposter was more successful intest 1. She gained 2 positive LLRs for 2 modelsof enrolled speakers.In test 2 the world model was exchangedand models retrained. This world model wastrained on excerpts of spontaneous speech from109 young male speakers recorded with a similarquality as the enrolled speakers.LLR Score Test 2 Speaker RA-0,8-1Test SpeakerFigure 3. Result for test 1 speaker HV against allenrolled models.In the 3 rd test the correct model is highestranked again, however, the LLR (0.009) is low.LLR0,40,30,20,10-0,1-0,2-0,3-0,4-0,5-0,6M F M M F M M M F M M F MRA KC JA PN JV SS JaL JL EB TL HV UV AERA_t RA_t RA_t RA_t RA_t RA_t RA_t RA_t RA_t RA_t RA_t RA_t RA_tTest SpeakerLLR0-0,1-0,2-0,3-0,4-0,5-0,6-0,7-0,8LLR Score Test 1 Imposter Speaker MHM F M M M F M F F M M M MAE EB PN HV RA UV JA KC JV JaL JL TL SSMhimMhimMhimMhimMhimMhimMhimMhimMhimMhimMhimMhimMhimTest SpeakerFigure 6. Result for test 2 speaker RA against allenrolled models.The increase in data for world model traininghas had no significant effect in this case.0,60,40,2LLR Score Test 2 Speaker JAFigure 4. Result for test 1 imposter speaker MHagainst all enrolled models.The 1 st imposter speaker has no positive valuesand the system seems to successfully keep thedoor closed.LLR0-0,2-0,4-0,6M M M M M F F M M M M F FJA TL JL PN RA JV KC JaL AE SS HV UV EBJA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_t JA_tTest SpeakerFigure 7. Result for test 2 speaker JA against all enrolledmodels.196
Proceedings, FONETIK 2009, Dept. of Linguistics, Stockholm UniversityFor the test of speaker JA the new world modelimproved the test result significantly. The correctmodel now gets a very high score (0.53)and even though the second best has a positiveLLR (0.03) it is very low.LLRFigure 8. Result for test 2 speaker HV against allenrolled models.Also for this test the new world model improvesthe correct LLR and creates a larger distanceto the other models.LLRFigure 9. Result for test 2 impostor speaker MHagainst all enrolled models.In the male impostor test for test 2 we obtaineda rather peculiar result where the male impostorgets a positive LLR for a female target model.The lack of female training data in the worldmodel is most probably the explanation for that.LLR0,20,10-0,1-0,2-0,3-0,4-0,50,10-0,1-0,2-0,4-0,5-0,6LLR Score Test 2 Speaker HVLLR Score Test 2 Imposter Speaker MHF F M M M M M F F M M M MEB KC PN RA AE JA HV JV UV JL JaL SS TL-0,3 MhimMhimMhimMhimMhimMhimMhimMhimMhimMhimMhimMhimMhim0,80,70,60,50,40,30,20,10-0,1M M F M F F M M F M M M MHV AE UV SS KC EB RA JaL JV JL PN JA TLHV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_t HV_tTest SpeakerTest SpeakerLLR Score Test 2 Imposter Speaker HEM M F M M F M M F F M M MAE HV UV TL PN EB JaL JA JV KC RA JL SSHEimHEimHEimHEimHEimHEimHEimHEimHEimHEimHEimHEimHEimTest SpeakerFigure 10. Result for test 2 female impostor speakerHE against all enrolled models.When it comes to the female impostor the resultit becomes even clearer that female trainingdata is missing in the world model. All scoresexcept 1 are positive and some of the scoresvery high.ConclusionsThis first step included a successful implementationof open source tools, building a testframework and scripting procedures for text-independentspeaker comparison. A small pilotstudy on performance of high quality recordingswere made. We can conclude that it is notsufficient to train a UBM using only malespeakers if you want the system to be able tohandle any incoming voice. However, fordemonstration purposes and comparison betweensmall amounts of data it is sufficient touse the technique.ReferencesBoersma, Paul & Weenink, David (2009).Praat: doing phonetics by computer (Version5.1.04) [Computer program]. RetrievedApril 4, 2009, from http://www.praat.org/Bonastre, J-F, Wils, F. & Meigner, S. (2005)ALIZE, a free toolkit for speaker recognition,in Proceedings of ICASSP, 2005, pp.737–740.Bonastre, J-F, Scheffer, N., Matrouf, C., Fredouille,A., Larcher, A., Preti, A.,Pouchoulin, G., Evans, B., Fauve, B. & Mason,J.S. (2008) ALIZE/SpkDet: a state-ofthe-artopen source software for speaker recognition.In Odyssey 2008 - The Speakerand Language Recognition Workshop,2008.Eriksson, A. (2004) SweDia 2000: A Swedishdialect database. In Babylonian ConfusionResolved. Proc. Nordic Symposium on theComparison of Spoken Languages, ed. by P.J. Henrichsen, Copenhagen Working Papersin LSP 1 – 2004, 33–48Guillaume, G. (2004) SPro: speech signal processingtoolkit, Software available at http://gforge.inria.fr/projects/spro .Martin, A. F. and Przybocki, M. A. (1999) TheNIST 1999 Speaker Recognition Evaluation-AnOverview. Digital Signal Processing10: 1–18.Reynolds, D. A., Quatieri, T. F., Dunn, R. B.,(2000) Speaker Verification Using AdaptedGaussian Mixture Models, Digital SignalProcessing, 2000.197
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