Factors affecting the intelligibility of recorded speech: Considerations for forensic audio “best evidence”.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Derived from a traditional common law rule of evidence, the “best evidence” standard as applied to recorded audio prescribes that an original recording, and not a duplicated or altered copy, will be presented in legal proceedings. The intent of this standard is to ensure that the integrity of the original evidence is preserved, such that a court is reasonably assured that it is being presented with the most complete and accurate record of the evidence. However, when considering forensic audio recordings of speech, which are frequently made in adverse acoustic environments, presentation of such recordings in their original form may not afford a court with the opportunity for a complete and accurate assessment of the evidence in question—namely, what words are being spoken on the recording? The current paper summarizes the technological and listener-based factors that should be considered when speech intelligibility is of prime importance in meeting the best evidence standard for presentation of forensic audio in court proceedings. Illustrative examples from recent court cases will be provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it