Modelling speech reception thresholds and their improvements due to spatial noise reduction algorithms in bimodal cochlear implant users.
Hear Res 2022;
420:108507. [PMID:
35484022 PMCID:
PMC9188268 DOI:
10.1016/j.heares.2022.108507]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 11/22/2022]
Abstract
This paper compares two modelling approaches to predict the speech recognition ability of bimodal CI users and the benefit of using beamformers.
The modelling approaches vary in computational complexity and fitting requirements.
A complex cafeteria spatial scenario with three localized single noise source scenario and a diffuse multi-talker babble noise is used.
The automatic speech recognizer is more accurate across the different spatial scenarios and noise types and requires less fitting compared to the statistical modelling approach.
Spatial noise reduction algorithms (“beamformers”) can considerably improve speech reception thresholds (SRTs) for bimodal cochlear implant (CI) users. The goal of this study was to model SRTs and SRT-benefit due to beamformers for bimodal CI users. Two existing model approaches varying in computational complexity and binaural processing assumption were compared: (i) the framework of auditory discrimination experiments (FADE) and (ii) the binaural speech intelligibility model (BSIM), both with CI and aided hearing-impaired front-ends. The exact same acoustic scenarios, and open-access beamformers as in the comparison clinical study Zedan et al. (2021) were used to quantify goodness of prediction. FADE was capable of modeling SRTs ab-initio, i.e., no calibration of the model was necessary to achieve high correlations and low root-mean square errors (RMSE) to both, measured SRTs (r = 0.85, RMSE = 2.8 dB) and to measured SRT-benefits (r = 0.96). BSIM achieved somewhat poorer predictions to both, measured SRTs (r = 0.78, RMSE = 6.7 dB) and to measured SRT-benefits (r = 0.91) and needs to be calibrated for matching average SRTs in one condition. Greatest deviations in predictions of BSIM were observed in diffuse multi-talker babble noise, which were not found with FADE. SRT-benefit predictions of both models were similar to instrumental signal-to-noise ratio (iSNR) improvements due to the beamformers. This indicates that FADE is preferrable for modeling absolute SRTs. However, for prediction of SRT-benefit due to spatial noise reduction algorithms in bimodal CI users, the average iSNR is a much simpler approach with similar performance.
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