The Accuracy of Envelope Following Responses in Predicting Speech Audibility

Abstract

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 (f 0) 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 f 0. 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 T2, 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 T2 (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 T2 (88.4%). When analyzed using Hotelling’s T2 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.

Characteristics of Speech-Evoked Envelope Following Responses in Infancy

“…Smaller or no differences between infants and adults when infants receive a higher stimulus level parallel previous threshold-based studies in tone-evoked EFRs and ABRs ( Rance & Tomlin, 2006 ; Sininger et al., 1997 ). Increases in stimulus level lead to higher EFR amplitudes, SNR, and phase coherence ( Easwar et al., 2015b , 2020a , 2020b ). Likewise, development-driven changes often result in improved EFR characteristics.…”

“…The token /susa R i/ (2.05 s), spoken by a 42-year-old male (average f 0 ¼ 98 Hz) from Southwestern Ontario, was used as the EFR stimulus (Easwar et al, 2015b(Easwar et al, , 2015c(Easwar et al, , 2020a(Easwar et al, , 2020b. Speech recordings were made using a studio-grade microphone (AKG Type C 4000B) and SpectraPLUS software (v5.0.26.0; Pioneer Hill Software LLC, Poulsbo, WA, USA) and further modified using Praat (Boersma & Weenink, 2017), GoldWave (v5.58, GoldWave Inc., St. John's, Newfoundland, Canada), and MATLAB (Mathworks, Natick, MA, USA).…”

“…Envelope following responses (EFRs) may be a useful objective tool to evaluate the audibility of speech sounds in children who are unable to participate in behavioral hearing tests ( Easwar et al., 2015b , 2015c , 2020a ). Scalp-recorded EFRs reflect neural activity phase-locked to the stimulus envelope and have been elicited by a variety of speech stimuli, including naturally spoken vowels ( Aiken & Picton, 2006 ; Choi et al., 2013 ), synthesized vowels ( Anderson et al., 2015 ; Skoe et al., 2015 ), high- or low-pass filtered vowels ( Easwar et al., 2015b ; Vanheusden et al., 2019 ), individual vowel formants ( Easwar et al., 2015a , 2019 ; Laroche et al., 2013 ), and modified fricatives ( Easwar et al., 2015b , 2020b ).…”

The Influence of Sensation Level on Speech-Evoked Envelope Following Responses

“…Linear mixed effects models were used to assess the effect of SL on EFR amplitude and PC. Some EFRs, especially those recorded at low SLs, were not statistically different from the noise floor (Easwar et al 2020a). However, all EFR data, irrespective of their detection outcome, were included in the statistical analysis as they provide the best available estimate of amplitude and PC.…”

Objective Detection of the Speech Frequency Following Response (sFFR): A Comparison of Two Methods

“…Over the past two decades, the sFFR has been used in a variety of research studies examining: effects of auditory expertise and deprivation on speech feature encoding [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]; neural representation of speech in adverse listening conditions [ 8 , 9 , 10 ]; objective hearing aid fitting [ 11 ]; bimodal hearing assessment [ 12 , 13 , 14 ]; and auditory development [ 15 ], to name a few examples. Despite the broad use of this technique, few studies, e.g., [ 11 , 16 , 17 , 18 ] have examined statistically-based automated response detection of sFFRs. This consideration is important in both clinical and research domains, as automated detection would aid in objective response identification and would provide a statistical basis for stopping rules when recording sFFRs.…”