ArticleFrontiers in neuroscience2021
A Speech-Level-Based Segmented Model to Decode the Dynamic Auditory Attention States in the Competing Speaker Scenes.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Temporal Response Function-Driven Representational Similarity Analysis for Speech Perception Decoding with MEG and EEG.Biology · 2026Article
- Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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5 authors.
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Abstract
In the competing speaker environments, human listeners need to focus or switch their auditory attention according to dynamic intentions. The reliable cortical tracking ability to the speech envelope is an effective feature for decoding the target speech from the neural signals. Moreover, previous studies revealed that the root mean square (RMS)-level-based speech segmentation made a great contribution to the target speech perception with the modulation of sustained auditory attention. This study further investigated the effect of the RMS-level-based speech segmentation on the auditory attention decoding (AAD) performance with both sustained and switched attention in the competing speaker auditory scenes. Objective biomarkers derived from the cortical activities were also developed to index the dynamic auditory attention states. In the current study, subjects were asked to concentrate or switch their attention between two competing speaker streams. The neural responses to the higher- and lower-RMS-level speech segments were analyzed
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