Rating speech intelligibility using raw-audio as the input to a deep neural-network
Bibliographic record
Abstract
Objective measurement of speech intelligibility is a challenge when working with speech-impaired patients. Speech intelligibility scores (the average transcription accuracy across a set of words or sentences by a listener) are a common way of assessing disordered speech. Human-based measurements are less than ideal due to individual differences in listening ability, the time it takes to collect the measures, and other challenges. The present study investigates deep neural networks for fast, automatic, and objective speech intelligibility scoring of head-and-neck cancer patients. We assessed models using the raw acoustic signal as the input to a network with multiple convolutional layers. It is believed that when the raw acoustic signal is used as the input, a convolutional network acts as a filter bank optimized for intelligibility scoring. We report the model accuracy results of repeated training, testing, and comparison of different model structures. Further, we compare the results using a 10-fold cross-validation approach and report the average correlation between the predicted and actual values.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".