@inproceedings{c52d5b38e73a4d8b8d5343a11bda6ddc,
title = "Homogeneity measure for forensic voice comparison: A step forward reliability",
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.",
keywords = "Forensic voice comparison, Homogeneity, Reliability, Speaker recognition",
author = "Moez Ajili and Bonastre, \{Jean Fran{\c c}ois\} and Solange Rossato and Juliette Kahn and Itshak Lapidot",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-25751-8\_17",
language = "English",
isbn = "9783319257501",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "135--142",
editor = "Alvaro Pardo and Josef Kittler",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}