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Record W2075340462 · doi:10.1159/000229302

Simulated Phase-Locking Stimulation: An Improved Speech Processing Strategy for Cochlear Implants

2009· article· en· W2075340462 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueORL · 2009
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCochlear implantSpeech recognitionComputer scienceQUIETSpeech processingMasking (illustration)Envelope (radar)Noise (video)Speech perceptionAcousticsAudiologyArtificial intelligenceTelecommunicationsMedicinePhysicsPsychologyPerceptionNeuroscience

Abstract

fetched live from OpenAlex

The continuous interleaved sampling (CIS) speech-processing strategy has been widely used for cochlear implants to extract speech envelope information without preserving phase information. In this study, a novel simulated phase-locking stimulation (SPLS) strategy, which detects zero-crossing times of the narrow-band signal of each band, was developed to extract both phase and amplitude-envelope information from a bank of frequency bands of speech sounds. The advantage of the SPLS strategy over the CIS strategy was confirmed by the results of our psychophysical experiments, showing that normal-hearing Chinese listeners' performance in recognizing SPLS-processed Chinese speech was significantly better than their performance of recognizing CIS-processed Chinese speech under quiet, noise-masking, or speech-masking conditions. Thus, the results suggest that if the SPLS strategy is used to modulate the interval of electrical stimulation pulses in cochlear-implant devices according to extracted phase information, the speech-processing functions of cochlear implant devices would be improved for Chinese cochlear implant users.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.392
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it