Recognizing Voices Through a Cochlear Implant: A Systematic Review of Voice Perception, Talker Discrimination, and Talker Identification
Notice bibliographique
Résumé
OBJECTIVE: Some cochlear implant (CI) users report having difficulty accessing indexical information in the speech signal, presumably due to limitations in the transmission of fine spectrotemporal cues. The purpose of this review article was to systematically review and evaluate the existing research on talker processing in CI users. Specifically, we reviewed the performance of CI users in three types of talker- and voice-related tasks. We also examined the different factors (such as participant, hearing, and device characteristics) that might influence performance in these specific tasks. DESIGN: We completed a systematic search of the literature with select key words using citation aggregation software to search Google Scholar. We included primary reports that tested (a) talker discrimination, (b) voice perception, and (c) talker identification. Each report must have had at least one group of participants with CIs. Each included study was also evaluated for quality of evidence. RESULTS: The searches resulted in 1,561 references, which were first screened for inclusion and then evaluated in full. Forty-three studies examining talker discrimination, voice perception, and talker identification were included in the final review. Most studies were focused on postlingually deafened and implanted adult CI users, with fewer studies focused on prelingual implant users. In general, CI users performed above chance in these tasks. When there was a difference between groups, CI users performed less accurately than their normal-hearing (NH) peers. A subset of CI users reached the same level of performance as NH participants exposed to noise-vocoded stimuli. Some studies found that CI users and NH participants relied on different cues for talker perception. Within groups of CI users, there is moderate evidence for a bimodal benefit for talker processing, and there are mixed findings about the effects of hearing experience. CONCLUSIONS: The current review highlights the challenges faced by CI users in tracking and recognizing voices and how they adapt to it. Although large variability exists, there is evidence that CI users can process indexical information from speech, though with less accuracy than their NH peers. Recent work has described some of the factors that might ease the challenges of talker processing in CI users. We conclude by suggesting some future avenues of research to optimize real-world speech outcomes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,005 |
| Bibliométrie | 0,015 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».