Issues of bilingualism in likelihood ratio-based forensic voice comparison
Bibliographic record
Abstract
Situated at the intersection of forensic speech science and bilingualism, this thesis focuses on the issues of language and language mismatch in forensic voice comparison (FVC) and examines their effects on features commonly used in FVC within the framework of likelihood ratios (LRs). To this end, two experiments are presented which explore (1) the performance of the alveolar fricative /s/, long-term formant distributions (LTFDs) and automatic speaker recognition (ASR) software as speaker discriminants in same-language comparisons in Canadian English and French, and (2) the performance of the features above in cross-language comparisons, following a cross-linguistic acoustic analysis of the linguistic-phonetic features. \n \nAlthough /s/ showed stronger language-independence acoustically than LTFDs, results from Experiment 1 show that /s/ performed more strongly as a speaker discriminant in French than in English, whereas the performance of LTFDs and ASR in the two languages was similar. Results from Experiment 2 show poorer performance across all features to varying extents in cross-language comparisons, which was exacerbated when appropriate reference data matching the language conditions of the case were not used. Individual-level analysis further reveals a complex mapping between acoustic and individual performance in cross-language comparisons. In particular, speakers for whom LTFDs provided the strongest discriminatory performance did not necessarily show the lowest within-speaker variation. \n \nOverall, findings from the current study contribute to our understanding of cross-language comparisons, and more generally to the area of forensic speech science, by demonstrating quantitatively the impact of language mismatch on the discriminatory potential of different linguistic-phonetic and acoustic features within the numerical LR framework, as well as the significance of case-appropriate reference data in such cases. They also demonstrate the diagnostic value of individual-level analysis in system testing and indicate the need for a more nuanced conception of within- and between-speaker variability for FVC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".