Development and Evaluation of a Speech-to-Noise Ratio Feedback System
Bibliographic record
Abstract
People with Parkinson's disease (PD) often present reduced vocal loudness, which may impact the intelligibility of their speech especially in noisy environments. To address this issue, we developed an assistive speech-to-noise ratio feedback (SNF) system that estimates the user's speech signal-to-noise ratio (SNR) in real-time and activates an audible alarm if the SNR falls below a predefined threshold. The proposed SNF system is comprised of a pair of over-the-ear binaural microphones for audio data acquisition, and a mobile application (app) that implements the key algorithms for coherence-based own voice detection (OVD), speech SNR estimation, and alarm triggering. The proposed SNF system performance was evaluated through electroacoustic and subjective tests under a variety of environ-mental conditions. Our results indicated that the lightweight OVD algorithm effectively differentiated the user's own voice from other audio signals when appropriate thresholds were set. The subjective-testing results also demonstrated that, during use, the SNF system effectively increased users' speech intensity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".