The Accuracy of Envelope Following Responses in Predicting Speech Audibility
Notice bibliographique
Résumé
OBJECTIVES: The present study aimed to (1) evaluate the accuracy of envelope following responses (EFRs) in predicting speech audibility as a function of the statistical indicator used for objective response detection, stimulus phoneme, frequency, and level, and (2) quantify the minimum sensation level (SL; stimulus level above behavioral threshold) needed for detecting EFRs. DESIGN: In 21 participants with normal hearing, EFRs were elicited by 8 band-limited phonemes in the male-spoken token /susa∫i/ (2.05 sec) presented between 20 and 65 dB SPL in 15 dB increments. Vowels in /susa∫i/ were modified to elicit two EFRs simultaneously by selectively lowering the fundamental frequency (f0) in the first formant (F1) region. The modified vowels elicited one EFR from the low-frequency F1 and another from the mid-frequency second and higher formants (F2+). Fricatives were amplitude-modulated at the average f0. EFRs were extracted from single-channel EEG recorded between the vertex (Cz) and the nape of the neck when /susa∫i/ was presented monaurally for 450 sweeps. The performance of the three statistical indicators, F-test, Hotelling's T, and phase coherence, was compared against behaviorally determined audibility (estimated SL, SL ≥0 dB = audible) using area under the receiver operating characteristics (AUROC) curve, sensitivity (the proportion of audible speech with a detectable EFR [true positive rate]), and specificity (the proportion of inaudible speech with an undetectable EFR [true negative rate]). The influence of stimulus phoneme, frequency, and level on the accuracy of EFRs in predicting speech audibility was assessed by comparing sensitivity, specificity, positive predictive value (PPV; the proportion of detected EFRs elicited by audible stimuli) and negative predictive value (NPV; the proportion of undetected EFRs elicited by inaudible stimuli). The minimum SL needed for detection was evaluated using a linear mixed-effects model with the predictor variables stimulus and EFR detection p value. RESULTS: of the 3 statistical indicators were similar; however, at the type I error rate of 5%, the sensitivities of Hotelling's T (68.4%) and phase coherence (68.8%) were significantly higher than the F-test (59.5%). In contrast, the specificity of the F-test (97.3%) was significantly higher than the Hotelling's T (88.4%). When analyzed using Hotelling's T as a function of stimulus, fricatives offered higher sensitivity (88.6 to 90.6%) and NPV (57.9 to 76.0%) compared with most vowel stimuli (51.9 to 71.4% and 11.6 to 51.3%, respectively). When analyzed as a function of frequency band (F1, F2+, and fricatives aggregated as low-, mid- and high-frequencies, respectively), high-frequency stimuli offered the highest sensitivity (96.9%) and NPV (88.9%). When analyzed as a function of test level, sensitivity improved with increases in stimulus level (99.4% at 65 dB SPL). The minimum SL for EFR detection ranged between 13.4 and 21.7 dB for F1 stimuli, 7.8 to 12.2 dB for F2+ stimuli, and 2.3 to 3.9 dB for fricative stimuli. CONCLUSIONS: EFR-based inference of speech audibility requires consideration of the statistical indicator used, phoneme, stimulus frequency, and stimulus level.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».