NFI-FRITS: A forensic speaker recognition database and some first experiments

By D. V. D. Vloed, J. Bouten, D. V. Leeuwen. 2014

24 citations recorded by the source corpus.

In this paper we describe the collection of a speech database with forensically realistic data. It consists of speech material obtained from lawfully intercepted telephone conversations collected during police investigations. The speech material therefore is very similar to the kind we encounter in casework at the Netherlands Forensic Institute. The database is augmented with metadata describing language, accent, speaking style and acoustic conditions. A total of 604 speakers have been identified in 4188 conversation sides. After manual speaker attribution using various forms of available metadata, the speech content has been anonymised by zeroing out fragments that might disclose the real identity of speakers. Additional to the database description, this paper reports on some speaker recognition experiments using a commercially available forensic speaker recognition system. We can observe some effect of spoken language in terms of calibration, but overall the systems appears not too sensitive to accent or language.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

VoxCeleb2: Deep Speaker Recognition (2018, background): “, from telephone calls [23, 24] or acoustic laboratories [25, 26, 27]), (ii) they are manually annotated and hence limited in size [8], or (iii) not freely available to the speaker community [28, 27] (see [7] for a full review of existing datasets).”
VoxCeleb: A Large-Scale Speaker Identification Dataset (2017, background): “Many existing datasets are obtained under controlled conditions, for example: forensic data intercepted by police of-ﬁcials [16], data from telephone calls [17], speech recorded live in high quality environments such as acoustic laboratories [18, 19], or speech recorded from mobile devices [20, 21].”
The Speakers in the Wild (SITW) Speaker Recognition Database (2016, background): “These databases are focused on constrained conditions such as controlled collection of clean microphone speech [1, 2], clean speech captured from a mobile device [3], and telephone speech from forensic cases [4].”