Objective estimation of tracheoesophageal speech ratings using an auditory model
Bibliographic record
Abstract
Total laryngectomy is often the treatment of choice for many individuals diagnosed with advanced laryngeal cancer. This procedure alters the normal voice production mechanism, and tracheoesophageal (TE) speech is one alternative method of voicing postlaryngectomy. TE speech is created when pulmonary air is passed through the upper esophagus to create a vibratory source that is then articulated into speech. TE speech is often characterized by abnormal voice quality. Acoustic analysis of TE speech has the potential of quantifying the voice quality and assisting the speech language pathologist in facilitating rehabilitation. Motivated in part by the recent advances in telecommunication industry for speech quality estimation, this paper investigated the application of an auditory model in predicting the ratings of TE speech by normal hearing listeners. The Moore-Glasberg auditory model was employed to extract perceptually relevant features from the acoustic waveform, and these features were later combined to estimate the subjective ratings of TE speech. This approach was validated with a database of subjective ratings of speech samples recorded from 35 TE speakers. Results showed moderate correlations between the objective metrics and the subjective ratings, and these correlations were significantly better than those obtained with traditional methods used in the telecommunication applications.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".