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.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".