Can Judges Trust the I-Vectors Scores?: A Comparative Study of Voices Comparison in the Forensic Domain

1. Drygajlo, A., Haraksim, R.: Biometric evidence in forensic automatic speaker recognition. In: Handbook of Biometrics for Forensic Science, pp. 221–239. Springer (2017). https://doi.org/10.1007/978-3-319-50673-9_10

2. Drygajlo, A., Jessen, M., Gfroerer, S., Wagner, I., Vermeulen, J., Niemi, T.: Methodological guidelines for best practice in forensic semiautomatic and automatic speaker recognition. Verlag für Polizeiwissenschaft (2016)

   Google Scholar 

3. Morrison, G.S.: The impact in forensic voice comparison of lack of calibration and of mismatched conditions between the known-speaker recording and the relevant-population sample recordings. Forensic Sci. Int. 283, e1–e7 (2018)

   Article  Google Scholar 

4. Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted gaussian mixture models. Digit. Signal Process. 10(1–3), 19–41 (2000)

   Article  Google Scholar 

5. Burget, L., Plchot, O., Cumani, S., Glembek, O., Matějka, P., Brümmer, N.: Discriminatively trained probabilistic linear discriminant analysis for speaker verification. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4832–4835. IEEE (2011). https://doi.org/10.1109/ICASSP.2011.5947437

6. Lei, L., Kun, S.: Speaker recognition using wavelet cepstral coefficient, i-vector, and cosine distance scoring and its application for forensics. J. Electr. Comput. Eng. 2016 (2016)

   Google Scholar 

7. Tsuge, S., Ishihara, S.: Text-dependent forensic voice comparison: likelihood ratio estimation with the hidden Markov model (HMM) and Gaussian mixture model – universal background model (GMM-UBM) approaches. In: Proceedings of the Australasian Language Technology Association Workshop, pp. 17–25 (2018)

   Google Scholar 

8. Larcher, A., Bonastre, J.F., Fauve, B., Lee, K.A., Levy, C., Li, H., Mason, J., Parfait, J.Y.: ALIZE 3.0-open source toolkit for state-of-the-art speaker recognition. In: Annual Conference of the International Speech Communication Association, Lyon, France (2013)

   Google Scholar 

9. Kenny, P., Stafylakis, T., Ouellet, P., Alam, M.J.: JFA-based front ends for speaker recognition. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1705–1709. IEEE (2014). https://doi.org/10.1109/ICASSP.2014.6853889

10. Dehak, N., Kenny, P.J., Dehak, R., Dumouchel, P., Ouellet, P.: Front-end factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process. 19, 788–798 (2010)

    Article  Google Scholar 

11. Sadjadi, S.O., Slaney, M., Heck, L.: MSR identity toolbox v1. 0: a MATLAB toolbox for speaker-recognition research. In: Speech and Language Processing Technical Committee Newsletter, Piscataway, NJ, USA, pp. 1–32. IEEE Signal Processing Society (2013)

    Google Scholar 

12. Prince, S.J.D., Elder, J.H.: Probabilistic linear discriminant analysis for inferences about identity. In: IEEE 11th International Conference on Computer Vision 2007, Rio de Janeiro, pp. 1–8. IEEE (2007). https://doi.org/10.1109/ICCV.2007.4409052

13. Dehak, N., Dehak, R., Glass, J.R., Reynolds, D.A., Kenny, P.: Cosine similarity scoring without score normalization techniques. In: Odyssey, p. 15 (2010)

    Google Scholar 

14. Bu, H., Du, J., Na, X., Wu, B., Zheng, H.: Aishell-1: an open-source mandarin speech corpus and a speech recognition baseline. In: 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA) 2017, Seoul, pp. 1–5. IEEE (2017). https://doi.org/10.1109/ICSDA.2017.8384449

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