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Vowel Inherent Spectral Change in Forensic Voice Comparison

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Vowel Inherent Spectral Change

Part of the book series: Modern Acoustics and Signal Processing ((MASP))

Abstract

The onset + offset model of vowel inherent spectral change has been found to be effective for vowel-phoneme identification, and not to be outperformed by more sophisticated parametric-curve models. This suggests that if only simple cues such as initial and final formant values are necessary for signaling phoneme identity, then speakers may have considerable freedom in the exact path taken between the initial and final formant values. If the constraints on formant trajectories are relatively lax with respect to vowel-phoneme identity, then with respect to speaker identity there may be considerable information contained in the details of formant trajectories. Differences in physiology and idiosyncrasies in the use of motor commands may mean that different individuals produce different formant trajectories between the beginning and end of the same vowel phoneme. If within-speaker variability is substantially smaller than between-speaker variability then formant trajectories may be effective features for forensic voice comparison. This chapter reviews a number of forensic-voice-comparison studies which have used different procedures to extract information from formant trajectories. It concludes that information extracted from formant trajectories can lead to a high degree of validity in forensic voice comparison (at least under controlled conditions), and that a whole trajectory approach based on parametric curves outperforms an onset + offset model.

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Abbreviations

C llr :

Log-likelihood-ratio cost

DCT:

Discrete cosine transform

DNA:

Deoxyribonucleic acid

DTW:

Dynamic time warping

F1:

First formant

F2:

Second formant

F3:

Third formant

LPC:

Linear predictive coding

LR:

Likelihood ratio

MFCC:

Mel-frequency cepstral coefficient

MVKD:

Multivariate kernel density

VISC:

Vowel inherent spectral change

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Acknowledgments

Thanks to Philip Rose, Peter F. Assmann, and Stephen A. Zahorian for comments on earlier versions of this chapter. The writing of this chapter was supported by the Australian Research Council, the Australian Federal Police, New South Wales Police, Queensland Police, the National Institute of Forensic Science, the Australasian Speech Science and Technology Association, and the Guardia Civil via Linkage Project LP100200142. Unless otherwise explicitly attributed, the opinions expressed herein are those of the author and do not necessarily represent the policies or opinions of any of the above mentioned organizations or individuals.

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  1. Forensic Voice Comparison Laboratory, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, Australia

    Geoffrey Stewart Morrison

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Editors and Affiliations

  1. School of Electrical Engineering, and Telecommunications, University of New South Wales, Sydney, 2052, New South Wales, Australia

    Geoffrey Stewart Morrison

  2. School of Behavioral and, Brain Sciences, University of Texas at Dallas, Richardson, Richardson, TX, 75083, Texas, USA

    Peter F. Assmann

Appendix: Interpretation of Tippett Plots

Appendix: Interpretation of Tippett Plots

A graphical method for presenting the results of running a likelihood-ratio forensic-comparison system on a set of test data is a Tippett plot. Tippett plots were introduced in Meuwly (2001) (inspired by the work of C. F. Tippett and by Evett and Buckleton 1996), and are now a standard method for presenting results in likelihood-ratio forensic-voice-comparison research. Tippett plots provide more detailed information about the results than is available from a summary measure such as C llr . This appendix is an extract from Morrison (2010 Sect. 99.930) and provides a guide to the interpretation of Tippett plots.

Figures 10, 11, 12 provide a series of Tippett plots drawn on the basis of hypothetical sets of output from forensic-comparison systems. The lines rising to the right represent the results from same-speaker comparisons in the test set, the cumulative proportion of log likelihood ratios less than or equal to the value indicated on the x axis. The lines rising to the left represent the results from different-speaker comparisons in the test set, the cumulative proportion of log likelihood ratios greater than or equal to the value indicated on the x axis. (Some authors draw both same-speaker and different-speaker lines as the cumulative proportion of log likelihood ratios greater than or equal to the value indicated on the x axis.) In these hypothetical results the same-speaker and different-speaker lines are symmetrical and cross at a log likelihood ratio of zero; this need not be the case for real test results.

Fig. 12
figure 12

Tippett plot of hypothetical test results

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An ideal forensic-comparison system should produce a large positive log likelihood ratio for a same-origin comparison, and a large negative log likelihood ratio for a different-origin comparison. Large-magnitude log likelihood ratios which support the consistent-with-fact hypothesis are better than small-magnitude log likelihood ratios which support the consistent-with-fact hypothesis. Log likelihood ratios which support the contrary-to-fact hypothesis are bad, and the larger their magnitude the worse they are. Therefore, in Tippet plots the further apart the same-speaker and different-speaker lines (the further to the right the same-speaker line and the further to the left the different-speaker line) the better the results. The results presented in the Tippett plot in Fig. 11 are therefore better than those presented in the Tippett plot in Fig. 10.

Note, however, that (consistent with the C llr metric) log-likelihood-ratio results which support contrary-to-fact hypotheses are of greater concern than whether the consistent-with-fact log-likelihood-ratio results are relatively small or large—a system which minimizes support for contrary-to-fact hypotheses is preferable even if this leads to a reduction in its strength of support for consistent-with-fact hypotheses. The results presented in the Tippett plot in Fig. 12 are therefore also better than those presented in the Tippett plot in Fig. 10.

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Morrison, G.S. (2013). Vowel Inherent Spectral Change in Forensic Voice Comparison. In: Morrison, G., Assmann, P. (eds) Vowel Inherent Spectral Change. Modern Acoustics and Signal Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14209-3_11

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