DOI: 10.1101/471367Nov 17, 2018Paper

Predicting individual speech intelligibility from the neural tracking of acoustic- and phonetic-level speech representations

BioRxiv : the Preprint Server for Biology
Damien LesenfantsTom Francart


Objective: To objectively measure speech intelligibility of individual subjects from the EEG, based on neural tracking of different representations of speech: low-level acoustical, higher-level discrete, or a combination (FS). To compare each model's prediction of the speech reception threshold (SRT) for each individual with the behaviorally measured SRT. Methods: Nineteen participants listened to Flemish Matrix sentences presented at different signal-to-noise ratios (SNRs), corresponding to different levels of speech understanding. For different EEG frequency bands (delta, theta, alpha, beta or low-gamma), a model was built to predict the EEG signal from various speech representations: envelope, spectrogram, phonemes, phonetic features or a combination of the spectrogram and phonetic features, hereinafter-named FS. The same model was used for all subjects. For each subject, the model predictions were then compared to the actual EEG for the different SNRs, and the prediction accuracy in function of SNR was used to predict the SRT. Results: The model based on the FS speech representation and the delta EEG band (i.e., FS-delta) yielded the highest monotonicity of neural tracking in function of SNR. This model also outperformed th...Continue Reading

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