ClearAI: AI-Driven Speech Enhancement for Hypophonic Speech
Bibliographic record
Abstract
Hypophonia is a common speech symptom related to Parkinson's disease, affecting human comprehension and effective communication. Unlike dysarthric speech, hypophonic speech is characterized by its low volume and breathy voice, which makes it challenging to be heard and understood by human and voice-controllable systems especially in noisy environments. Conventional speech enhancement techniques, primarily focusing on amplifying audio power or cancelling environmental noise, fall short in improving the intelligibility and perception for hypophonic speech. To enhance hypophonic speech, we present ClearAI, an innovative AI-powered technology to improve speech quality for individuals suffering from hypophonia. ClearAI first leverages voice conversion technology to create a parallel dataset composed of normal and corresponding hypophonic speech samples. Then, ClearAI incorporates a predictive model trained on augmented parallel data to estimate the optimal audio style from hypophonic speech to strengthen the audio intensity and enhance the speech patterns. Next, a speech restoration model is built on the generated parallel speech data to reconstruct clear speech from the style transferred speech. Our experimental results reveal that ClearAI leads to substantial improvements in audio intensity in both digital formats and over-the-air transmission. In addition, ClearAI successfully reduces the hypophonic speech recognition error rate by more than 30% in noisy environments. Our human test results also validate ClearAI enhanced speech has the best human perceptual quality compared with other baseline methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".