It's About Time: Minimizing Hardware and Software Latencies in Speech Research With Real-Time Auditory Feedback
Bibliographic record
Abstract
Purpose Various aspects of speech production related to auditory-motor integration and learning have been examined through auditory feedback perturbation paradigms in which participants' acoustic speech output is experimentally altered and played back via earphones/headphones "in real time." Scientific rigor requires high precision in determining and reporting the involved hardware and software latencies. Many reports in the literature, however, are not consistent with the minimum achievable latency for a given experimental setup. Here, we focus specifically on this methodological issue associated with implementing real-time auditory feedback perturbations, and we offer concrete suggestions for increased reproducibility in this particular line of work. Method Hardware and software latencies as well as total feedback loop latency were measured for formant perturbation studies with the Audapter software. Measurements were conducted for various audio interfaces, desktop and laptop computers, and audio drivers. An approach for lowering Audapter's software latency through nondefault parameter specification was also tested. Results Oft-overlooked hardware-specific latencies were not negligible for some of the tested audio interfaces (adding up to 15 ms). Total feedback loop latencies (including both hardware and software latency) were also generally larger than claimed in the literature. Nondefault parameter values can improve Audapter's own processing latency without negative impact on formant tracking. Conclusions Audio interface selection and software parameter optimization substantially affect total feedback loop latency. Thus, the actual total latency (hardware plus software) needs to be correctly measured and described in all published reports. Future speech research with "real-time" auditory feedback perturbations should increase scientific rigor by minimizing this latency.
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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 | 0.023 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".