Voice conversion in cochlear implantation
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Voice conversion algorithms may benefit cochlear implant (CI) users who better understand speech produced by one talker than by another. It is unclear how the source or target talker's fundamental frequency (F0) information may contribute to perception of converted speech. This study evaluated voice conversion algorithms for CI users in which the source or target talker's F0 was included in the converted speech. STUDY DESIGN: Development and evaluation of computerized voice conversion algorithms in CI patients. METHODS: A series of cepstral analysis-based algorithms were developed and evaluated in six CI users. The algorithms converted talker voice gender (male-to-female, or female-to-male); either the source or target talker F0 was included in the converted speech. The voice conversion algorithms were evaluated in terms of recognition of IEEE sentences, speech quality, and voice gender discrimination. RESULTS: Voice gender recognition performance showed that listeners strongly cued to the F0 that was included within the converted speech. For both IEEE sentence recognition and voice quality ratings, performance was poorer with the voice conversion algorithms than with original speech. Performance on female-to-male conversion was superior to male-to-female conversion. CONCLUSION: The strong cueing to F0 within the voice conversion algorithms suggests that CI users are able to utilize temporal periodicity information for some pitch-related tasks. Limitations on spectral channel information experienced by CI users may result in poorer performance with voice conversion algorithms due to distortion of speech formant information and degradation of the spectral envelope.
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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 | 0.000 | 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 | 0.002 |
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 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".