Modeling Binaural Speech Understanding in Complex Situations

Lavandier, Mathieu; Best, Virginia

doi:10.1007/978-3-030-00386-9_19

Cited by 11 publications

(12 citation statements)

References 99 publications

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“…One might be particularly interested in predicting the intelligibility of speech among competing talkers. No current model is able to do so [9]. Given the models currently available, those validated for non-stationary noise maskers seem to be the most appropriate to evaluate intelligibility differences associated with variations in energetic masking across speech-in-speech conditions.…”

Section: Discussionmentioning

confidence: 99%

“…To predict the overall effect of binaural hearing, the effective SNR is obtained by adding the binaural unmasking advantage to the better-ear SNR, assuming additive contributions 3 of the two mechanisms. This assumption, previously discussed [5,19,20] and not necessarily used in all binaural intelligibility models (see [9] for a review), allowed for accurate predictions of several data sets where the two mechanisms were involved both in isolation and combination [19,20]. The model does not provide an absolute evaluation of intelligibility, the prediction method is relative.…”

Section: Common Structurementioning

confidence: 99%

“…This room dependence might indicate an inherent limitation of the approach and could partially explain the wide range of early/late limits encountered in the literature. 9 To overcome this limitation, one could try to make the early/late separation room-dependent [51,52], or one might change the definition of useful/early and detrimental/late. Rennies et al (2019) [53] recently showed that a reflection could be characterized as useful or detrimental depending on whether it is binaurally useful/detrimental, i.e.…”

Section: Limitationsmentioning

confidence: 99%

“…Second, while being monaural, this model has the same underlying philosophy and structure as the other (binaural) models in the series. It is an SNR-based model [9] that uses the same signal as inputs and produces a similar effective SNR as output. The SNR analysis is very similar to the better-ear SNR analysis of the binaural models, while the E-C mechanism [4] underlying binaural unmasking is replaced here by harmonic cancellation (that is then based on the F0 of the masker rather than on its ITDs).…”

Section: Monaural Model For a Stationary Harmonic Maskermentioning

confidence: 99%

“…Several binaural models have been proposed to predict the effect of SRM on speech intelligibility. They are also presented in a recent review [9]. The aim of the present paper is to compare the model versions within a series of SNR-based intelligibility models that have been developed since the original version proposed by Lavandier and Culling (2010) [10].…”

Section: Introductionmentioning

confidence: 99%

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A series of SNR-based speech intelligibility models in the Auditory Modeling Toolbox

2022

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This technical paper presents a series of speech intelligibility models that have been developed since the original version proposed by Lavandier and Culling [(2010). Journal of the Acoustical Society of America 127, 387–399]. This binaural model accounts for better-ear listening and binaural unmasking to predict the intelligibility of a near-field target speech among multiple stationary noise sources in rooms for normal-hearing listeners. Subsequent model versions allowed to consider a reverberated speech target in the far-field, envelope-modulated noise sources, and hearing-impaired listeners. As an intermediate step before considering speech maskers, a monaural version incorporating a harmonic-cancellation mechanism was recently developed to account for the effect of a stationary harmonic masker. This technical review is oriented towards model users and explains when and how each model should be used, points at its advantages and limitations, and provides an example of predictions using a data set from the literature. All these models along with the data, signals and code used to prepare the presented figures are made available within the Auditory Modeling Toolbox (AMT 1.1).

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Section: Discussionmentioning

confidence: 99%

Section: Common Structurementioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Monaural Model For a Stationary Harmonic Maskermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A series of SNR-based speech intelligibility models in the Auditory Modeling Toolbox

2022

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Binaural Beamforming Taking Into Account Spatial Release From Masking

de Vries,

van de Par,

Leus

et al. 2024

IEEE/ACM Trans. Audio Speech Lang. Process.

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Individual differences in speech intelligibility at a cocktail party: A modeling perspective

Lavandier

Mason

Baltzell

et al. 2021

The Journal of the Acoustical Society of America

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This study aimed at predicting individual differences in speech reception thresholds (SRTs) in the presence of symmetrically placed competing talkers for young listeners with sensorineural hearing loss. An existing binaural model incorporating the individual audiogram was revised to handle severe hearing losses by (a) taking as input the target speech level at SRT in a given condition and (b) introducing a floor in the model to limit extreme negative better-ear signal-to-noise ratios. The floor value was first set using SRTs measured with stationary and modulated noises. The model was then used to account for individual variations in SRTs found in two previously published data sets that used speech maskers. The model accounted well for the variation in SRTs across listeners with hearing loss, based solely on differences in audibility. When considering listeners with normal hearing, the model could predict the best SRTs, but not the poorer SRTs, suggesting that other factors limit performance when audibility (as measured with the audiogram) is not compromised.

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Modeling Binaural Speech Understanding in Complex Situations

Cited by 11 publications

References 99 publications

A series of SNR-based speech intelligibility models in the Auditory Modeling Toolbox

A series of SNR-based speech intelligibility models in the Auditory Modeling Toolbox

Binaural Beamforming Taking Into Account Spatial Release From Masking

Individual differences in speech intelligibility at a cocktail party: A modeling perspective

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