The DyViS database: style-controlled recordings of 100 homogeneous speakers for forensic phonetic research

Authors

  • Francis Nolan Department of Linguistics, University of Cambridge
  • Kirsty McDougall Department of Linguistics, University of Cambridge
  • Gea de Jong Department of Linguistics, University of Cambridge
  • Toby Hudson Department of Linguistics, University of Cambridge

DOI:

https://doi.org/10.1558/ijsll.v16i1.31

Keywords:

forensic phonetic research, population data, simulated police interview technique, stylistic variation, dynamic features of speech, Standard Southern British English

Abstract

The DyViS project (‘Dynamic Variability in Speech: a Forensic Phonetic Study of British English’) at the University of Cambridge has compiled a large-scale database of speech recordings which will be freely available for (non-commercial) research purposes. The database comprises recordings of 100 male speakers of Standard Southern British English, aged 18-25, undertaking four tasks involving different speaking styles: a simulated police interview, a telephone call with an ‘accomplice’, a reading passage, and a set of read sentences. This paper describes the motivation for developing the DyViS database and explains its structure, including the novel techniques developed for eliciting spontaneous yet phonetically controlled speech under simulated forensic conditions.

Author Biographies

  • Francis Nolan, Department of Linguistics, University of Cambridge
    Francis Nolan is Professor of Phonetics in the Department of Linguistics at the University of Cambridge. His research interests range over phonetic theory, intonation, connected speech processes and speaker characteristics. His long-standing involvement in forensic phonetics covers both fundamental research and casework, including technical speaker identification and the use of voice parades. He believes that forensic phonetic practice needs to be underpinned by advances in phonetic theory. He is a founding member of IAFPA.
  • Kirsty McDougall, Department of Linguistics, University of Cambridge
    Kirsty McDougall is a post-doctoral research associate in the Department of Linguistics, University of Cambridge, working on the forensic phonetic research projects DyViS and VoiceSim. She has a B.A. in linguistics and a B.Sc. in mathematics and statistics from the University of Melbourne, and an M.Phil. and Ph.D. in linguistics from the University of Cambridge. Her research interests include speaker characteristics, theories of speech production, and phonetic realisation of varieties of English. She is a member of IAFPA.
  • Gea de Jong, Department of Linguistics, University of Cambridge
    Gea de Jong is a senior research associate in the Department of Linguistics, University of Cambridge where she has been working on the DyViS project: a forensic study of British English. Her MPhil degree was in Computer Speech and Language Processing from the University of Cambridge. She has a PhD in Linguistics from the University of Florida. She is the Director of Forensic Research Associates: since 1994 she has been consulted on issues of forensic speech and language analysis, including voice comparison and voice parades. She is a member of IAFPA.
  • Toby Hudson, Department of Linguistics, University of Cambridge
    Toby Hudson is a research assistant in the Department of Linguistics, University of Cambridge where he has been working on the forensic phonetic research projects DyViS and VoiceSim. He has a B.A. and an M.Phil. in Classics from the University of Cambridge. His research interests are in historical linguistics, and phonetics and phonology, especially forensic phonetics, tone and African languages. He is a member of IAFPA.

Published

2009-09-18

Issue

Section

Articles

How to Cite

Nolan, F., McDougall, K., de Jong, G., & Hudson, T. (2009). The DyViS database: style-controlled recordings of 100 homogeneous speakers for forensic phonetic research. International Journal of Speech, Language and the Law, 16(1), 31-57. https://doi.org/10.1558/ijsll.v16i1.31

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