Abstract
In forensic voice comparison, it is strongly recommended to follow the Bayesian paradigm to present a forensic evidence to the court. In this paradigm, the strength of the forensic evidence is summarized by a likelihood ratio (LR). But in the real world, to base only on the LR without looking to its degree of reliability does not allow experts to have a good judgement. This work is mainly motivated by the need to quantify this reliability. In this concept, we think that the presence of speaker specific information and its homogeneity between the two signals to compare should be evaluated. This paper is dedicated to the latter, the homogeneity. We propose an information theory based homogeneity measure which determines whether a voice comparison is feasible or not.
Chapter PDF
References
Champod, C., Meuwly, D.: The inference of identity in forensic speaker recognition. Speech Communication, 193–203 (2000)
Brummer, N., van Leeuwen, D.A.: On calibration of language recognition scores. In: Speaker and Language Recognition Workshop, pp. 1–8. IEEE Odyssey (2006)
Brummer, N., Doddington, G.: Likelihood-ratio calibration using prior-weighted proper scoring rules (2013). arXiv preprint arXiv:1307.7981
Bonastre, J.F., Bimbot, F., Boë, L.J., Campbell, J.P., Reynolds, D.A., Magrin-Chagnolleau, I.: Person authentication by voice: a need for caution. In: INTERSPEECH (2003)
Campbell, J.P., Shen, W., Campbell, W.M., Schwartz, R., Bonastre, J.-F., Matrouf, D.: Forensic speaker recognition. Institute of Electrical and Electronics Engineers (2009)
Morrison, G.S.: Forensic voice comparison and the paradigm shift. Science & Justice, 298–308 (2009)
Rose, P.: Technical forensic speaker recognition: Evaluation, types and testing of evidence. Computer Speech & Language, 159–191 (2006)
Morrison, G.S., Zhang, C., Rose, P.: An empirical estimate of the precision of likelihood ratios from a forensic-voice-comparison system. Forensic science international, 59–65 (2011)
Morrison, G.S.: Measuring the validity and reliability of forensic likelihood-ratio systems. Science & Justice, 91–98 (2011)
Campbell, W.M., Reynolds, D.A., Campbell, J.P., Brady, K.: Estimating and evaluating confidence for forensic speaker recognition. In: ICASSP, pp. 717–720 (2005)
Mengusoglu, E., Leich, H.: Confidence Measures for Speech/Speaker Recognition and Applications on Turkish LVCSR. PhD Faculte Polytechnique de Mons (2004)
Rao, W., Mak, M.W.: Boosting the performance of i-vector based speaker verification via utterance partitioning. IEEE Transactions on Audio, Speech and Language Processing, 1012–1022 (2013)
Greenberg, C.S., Stanford, V.M., Martin, A.F., Yadagiri, M., Doddington, G.R., Godfrey, J.J., Hernandez-Cordero, J.: The 2012 NIST speaker recognition evaluation. In: INTERSPEECH, pp. 1971–1975 (2013)
Kahn, J., Audibert, N., Rossato, S., Bonastre, J.F.: Intra-speaker variability effects on speaker verification performance. In: Odyssey, p. 21 (2010)
Matrouf, D., Scheffer, N., Fauve, B.G., Bonastre, J.F.: A straightforward and efficient implementation of the factor analysis model for speaker verification. In: INTERSPEECH, pp. 1242–1245 (2007)
Larcher, A., Bonastre, J.F., Fauve, B.G., Lee, K.A., Lévy, C., Li, H., Parfait, J.Y.: ALIZE 3.0-open source toolkit for state-of-the-art speaker recognition. In: INTERSPEECH, pp. 2768–2772 (2013)
Dehak, N., Kenny, P., Dehak, R., Dumouchel, P., Ouellet, P.: Front-end factor analysis for speaker verification. IEEE Transactions on Audio, Speech, and Language Processing, 788–798 (2011)
Prince, S.J.D., Elder, J.H.: Probabilistic linear discriminant analysis for inferences about identity. IEEE 11th International Conference on Computer Vision, ICCV (2007)
Brummer, N., du Preez, J.: Application-independent evaluation of speaker detection. Computer Speech and Language, 230–275 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ajili, M., Bonastre, JF., Rossato, S., Kahn, J., Lapidot, I. (2015). Homogeneity Measure for Forensic Voice Comparison: A Step Forward Reliability. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)