Consonant identification in noise using Hilbert-transform temporal fine-structure speech and recovered-envelope speech for listeners with normal and impaired hearing

Léger, Agnès C.; Reed, Charlotte M.; Desloge, Joseph G.; Swaminathan, Jaichander; Braida, Louis D.

doi:10.1121/1.4922949

Cited by 14 publications

(13 citation statements)

References 46 publications

(88 reference statements)

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“…In contrast, we found a 512 significant effect of the band number for spectral correlation. This is consistent with 513 previous findings that spectral resolution modulates the efficiency of extracting temporal 514 information from the TFS (Léger et al 2015;Oxenham 2018). 515…”

Section: Extraction Of Temporal Information From Tfs Depends On the Nsupporting

confidence: 84%

Speech Fine Structure Contains Critical Temporal Cues to Support Speech Segmentation

Teng

Cogan

Poeppel

2018

Preprint

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1Segmenting the continuous speech stream into units for further perceptual and linguistic 2 analyses is fundamental to speech recognition. The speech amplitude envelope (SE) has 3 long been considered a fundamental temporal cue for segmenting speech. Does the 4 temporal fine structure (TFS), a significant part of speech signals often considered to 5 contain primarily spectral information, contribute to speech segmentation? Using 6 magnetoencephalography, we show that the TFS entrains cortical oscillatory responses 7 between 3-6 Hz and demonstrate, using mutual information analysis, that (i) the temporal 8 information in the TFS can be reconstructed from a measure of frame-to-frame spectral 9 change and correlates with the SE and (ii) that spectral resolution is key to the extraction 10 of such temporal information. Furthermore, we show behavioural evidence that, when the 11 SE is temporally distorted, the TFS provides cues for speech segmentation and aids 12 speech recognition significantly. Our findings show that it is insufficient to investigate 13 solely the SE to understand temporal speech segmentation, as the SE and the TFS derived 14 from a band-filtering method convey comparable, if not inseparable, temporal 15 information. We argue for a more synthetic view of speech segmentationthe auditory 16 system groups speech signals coherently in both temporal and spectral domains. 17 18

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Section: Extraction Of Temporal Information From Tfs Depends On the Nsupporting

confidence: 84%

Speech Fine Structure Contains Critical Temporal Cues to Support Speech Segmentation

Teng

Cogan

Poeppel

2018

Preprint

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“…This research derives from previous studies which suggest that the amplitude variations present in the speech signal may play a role in determining the size of masking release in HI listeners (L eger et al, 2015;Reed et al, 2016). Specifically, by reducing amplitude variation for speech in interrupted noise, the audibility of lower-energy speech present in the noise dips is increased leading to higher intelligibility.…”

Section: Introductionmentioning

confidence: 99%

“…This problem may be compounded by the reduced or even absent ability of hearing-impaired (HI) listeners, relative to that of normal-hearing (NH) listeners, to take advantage of temporal fluctuations in background interference to understand the target speech (e.g., Festen and Plomp, 1990;Moore et al, 1999;Lorenzi et al, 2006;Bernstein and Grant, 2009;Desloge et al, 2010;L eger et al, 2015). The release from masking observed in fluctuating noise for NH listeners has been described in terms of a decrease in the signal-to-noise ratio (SNR) required to understand the target speech at a given level of performance (e.g., Festen and Plomp, 1990), or as an increase in performance for a fluctuating noise background compared to continuous noise at the same long-term root-mean-square (rms) level.…”

Section: Introductionmentioning

confidence: 99%

Masking release for hearing-impaired listeners: The effect of increased audibility through reduction of amplitude variability

Desloge

Reed

Braida

et al. 2017

The Journal of the Acoustical Society of America

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The masking release (i.e., better speech recognition in fluctuating compared to continuous noise backgrounds) observed for normal-hearing (NH) listeners is generally reduced or absent in hearing-impaired (HI) listeners. One explanation for this lies in the effects of reduced audibility: elevated thresholds may prevent HI listeners from taking advantage of signals available to NH listeners during the dips of temporally fluctuating noise where the interference is relatively weak. This hypothesis was addressed through the development of a signal-processing technique designed to increase the audibility of speech during dips in interrupted noise. This technique acts to (i) compare short-term and long-term estimates of energy, (ii) increase the level of short-term segments whose energy is below the average energy, and (iii) normalize the overall energy of the processed signal to be equivalent to that of the original long-term estimate. Evaluations of this energy-equalizing (EEQ) technique included consonant identification and sentence reception in backgrounds of continuous and regularly interrupted noise. For HI listeners, performance was generally similar for processed and unprocessed signals in continuous noise; however, superior performance for EEQ processing was observed in certain regularly interrupted noise backgrounds.

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“…A NMR (as defined by Léger et al., 2015) was calculated as: where FN is the score in fluctuating noise, CN is the score in continuous noise, and BN is the score in baseline noise.…”

Section: Methodsmentioning

confidence: 99%

Effect of Energy Equalization on the Intelligibility of Speech in Fluctuating Background Interference for Listeners With Hearing Impairment

et al. 2017

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The masking release (MR; i.e., better speech recognition in fluctuating compared with continuous noise backgrounds) that is evident for listeners with normal hearing (NH) is generally reduced or absent for listeners with sensorineural hearing impairment (HI). In this study, a real-time signal-processing technique was developed to improve MR in listeners with HI and offer insight into the mechanisms influencing the size of MR. This technique compares short-term and long-term estimates of energy, increases the level of short-term segments whose energy is below the average energy, and normalizes the overall energy of the processed signal to be equivalent to that of the original long-term estimate. This signal-processing algorithm was used to create two types of energy-equalized (EEQ) signals: EEQ1, which operated on the wideband speech plus noise signal, and EEQ4, which operated independently on each of four bands with equal logarithmic width. Consonant identification was tested in backgrounds of continuous and various types of fluctuating speech-shaped Gaussian noise including those with both regularly and irregularly spaced temporal fluctuations. Listeners with HI achieved similar scores for EEQ and the original (unprocessed) stimuli in continuous-noise backgrounds, while superior performance was obtained for the EEQ signals in fluctuating background noises that had regular temporal gaps but not for those with irregularly spaced fluctuations. Thus, in noise backgrounds with regularly spaced temporal fluctuations, the energy-normalized signals led to larger values of MR and higher intelligibility than obtained with unprocessed signals.

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Consonant identification in noise using Hilbert-transform temporal fine-structure speech and recovered-envelope speech for listeners with normal and impaired hearing

Cited by 14 publications

References 46 publications

Speech Fine Structure Contains Critical Temporal Cues to Support Speech Segmentation

Speech Fine Structure Contains Critical Temporal Cues to Support Speech Segmentation

Masking release for hearing-impaired listeners: The effect of increased audibility through reduction of amplitude variability

Effect of Energy Equalization on the Intelligibility of Speech in Fluctuating Background Interference for Listeners With Hearing Impairment

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