Current Conclusion Framework in the UK

The current practice for presenting FSC conclusions in a UK court is not in the form of an LR as described in § 2.2, but rather is that described in the UK Position Statement that was introduced in 2007. The UK Position Statement was motivated by concerns about the framework in which conclusions are typically

54

expressed in forensic speaker comparison cases” (French and Harrison 2007 p. 137). The UK Position Statement stemmed from ruling of the Appeal Court of England and Wales in R v. Doheny and Adams (1996), which showed that the interpretation of the DNA evidence at the initial trial had been flawed by the prosecutor’s fallacy (French and Harrison 2007). The introduction of the UK Position Statement signified a shift in the role of the forensic phonetician when presenting speech evidence. The foreword to the UK Position Statement suggests that experts in the past were often trying to identify speakers (French and Harrison, 2007, p. 138). However, under their new approach an expert would not be making identifications per se. Instead, the expert will take on a different role (not one of speaker identification), to provide an assessment of whether the voice in the questioned recordings fits the description of the suspect” (French and Harrison 2007 p. 138). The UK Position Statement was also proposed with the intention of aligning the field of FSC with more modern thinking” forensic sciences (French and Harrison 2007 p. 137).

The framework laid out in the UK Position Statement diverges from previous FSC conclusions by offering a framework which involves a bipartite assessment. The conclusion framework set out in French and Harrison (2007) potentially involves a two-part decision. The first part concerns the assessment of whether the samples are consistent with having been spoken by the same person. The second part, which only comes into play if there is a positive decision concerning consistency, involves an evaluation of how unusual or distinctive the combination of features that are common to the samples may be. An illustrated version of the UK Position Statement is provided in Figure 2.1.

55

Figure 2.1: Illustration of the UK Position Statement (Rose and Morrison, 2009, p.141)

The UK Position Statement is illustrated in Figure 2.1, where the decisions of consistency and distinctiveness are serially ordered. A consistency decision has three possible options: consistent, not-consistent, and no-decision. If a conclusion about consistency cannot be made, then the expert concludes with a single evaluation (i.e. not consistent, or no decision). In the event that the expert finds the two speech samples to be consistent, s/he will then assess the

56

distinctiveness. The degree of distinctiveness is made on a five-point impressionistic scale, ranging from not distinctive” to exceptionally distinctive”. The assessment of distinctiveness in many cases must draw upon the experience of the expert so that he/she can provide a statement of the typicality of the criminal speech sample.

The UK Position Statement framework can be seen as a transitional point, or a stepping stone, between a frequentist probability and an LR, where it is not providing a single probability of the hypothesis (e.g. the speaker in the criminal sample is likely to be person X), but not quite meeting the logical framework of the LR. At first glance the judgments of consistency and distinctiveness appear to mirror the numerator and denominator of an LR, as the consistency and distinctiveness account for both the similarity and the typicality of the speech recordings. However, the inner workings of the Position Statement do not hold true to the logical framework of an LR. There are two main reasons for this mismatch: (1) assessments are made on different scales, and (2) there is no logical procedure for combining (and weighing) constituent speech parameter evidence from a single case.

Rose and Morrison describe the assessment of consistency in the UK Position Statement as being on a three-point scale (Rose and Morrison, 2009, p. 142). Although Rose and Morrison acknowledge that the decision about consistency is categorical, one could argue that the assessment of consistency is more accurately described as simply a ternary decision (rather than on a three- point scale). This is due to the fact that the judgment is wholly categorical, and the ternary decision cannot be intuitively placed on a scale. A scale would imply some degree of hierarchy, and it is difficult to argue that, for example, no-

57

decision should be ranked before inconsistent (or vice versa). Therefore, the assessment of consistency is discrete in nature and does not offer a gradient assessment of the similarity (through quantification of the speech evidence), as the numerical LR would ultimately provide. The assessment of distinctiveness is on a scale of one to five; however, this does not follow the same logic as the assessments of consistency. Thus, it is difficult to establish a working relationship between the two assessments; instead they exist more as two separate entities, where practitioners are trying to make a judgment on the same piece of evidence.

The use of a five-point scale in the UK Position Statement makes the framework prone to a cliff-edge effect (Aitken and Taroni, 2004). By imposing defined boundaries an expert is faced with a hard decision. So, for example, if a criminal sample has an F0 mean of 115 Hz, while the population mean is 90 Hz, should the analysis of a speech sample lend itself to a distinctiveness assessment of 3 (distinctive) or 4 (highly distinctive)? Should the boundary between distinctive and highly distinctive be two standard deviations, or perhaps three? Forcibly imposing categorical boundaries could potentially over- or under-estimate the strength of evidence. Although the UK Position Statement is susceptible to the cliff-edge effect, the same can actually be said for the verbal LR scale provided by Evett (1998). Although the verbal scale suggested by Evett (1998) is associated with Log10 LRs, the cliff-edge effect can still occur for those Log10 LRs that lie close to the categorical boundaries.

The second inconsistency between the UK Position Statement and the LR is the lack of a protocol for combining the strength of evidence of individual phonetic-linguistic parameters. Under a Bayesian framework an expert is

58

expected to combine individual LRs for parameters that are mutually independent (Kononenko, 1990) by multiplying their LRs. If an expert is to naïvely combine correlated parameters without using appropriate statistical weightings, s/he then runs the risk of over-or under-estimating the strength of evidence (as s/he are essentially considering the same evidence multiple times). Under the UK Position Statement, experts make assessments of consistency and distinctiveness by informally considering all of the constituent pieces of analysis together. As such, they are unlikely to adequately (or transparently) consider the degree of correlation between the evidence. Therefore, conclusions made under the UK Position Statement framework could over- or under-estimate the strength of evidence.

Despite the disparity between the UK Position Statement and the LR, the UK Position Statement possesses two highly attractive attributes. Firstly, it allows the expert to avoid the undesirable and lengthy task of collecting (quantitative, data-based) population statistics for all possible relevant populations that could ever be required for a FSC case. Secondly, the framework allows the expert to avoid the difficulty of calculating numerical LRs for all phonetic-linguistic parameter distributions that do not fit into already existing LR algorithms. These two attributes are technically part and parcel of the same thing, as they together evaluate the denominator of the LR; however, the modeling of phonetic-linguistic parameters is also pertinent to the numerator. It is safe to argue that no LR algorithm could ever account for or encompass the full complexity of speech data; therefore, perhaps the UK Position Statement is right to circumvent fully quantitative population statistics and complicated models for calculating LRs for all speech parameters. Through experience and

59

education, an expert is able to account for instances of accommodation, channel mismatch, intoxication, emotional effects, and social factors. These factors tend to manifest themselves differently in the speech of each individual speaker and at different times. To create an algorithm that accounts for every individual, in every instance, would be near impossible.