Modulation masking and fine structure shape neural envelope coding to predict speech intelligibility across diverse listening conditions

A fundamental question in the neuroscience of everyday communication is how scene acoustics shape the neural processing of attended speech sounds and in turn impact speech intelligibility. While it is well known that the temporal envelopes in target speech are important for intelligibility, how the...

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Veröffentlicht in:The Journal of the Acoustical Society of America 2021-09, Vol.150 (3), p.2230-2244
Hauptverfasser: Viswanathan, Vibha, Bharadwaj, Hari M., Shinn-Cunningham, Barbara G., Heinz, Michael G.
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Sprache:eng
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Zusammenfassung:A fundamental question in the neuroscience of everyday communication is how scene acoustics shape the neural processing of attended speech sounds and in turn impact speech intelligibility. While it is well known that the temporal envelopes in target speech are important for intelligibility, how the neural encoding of target-speech envelopes is influenced by background sounds or other acoustic features of the scene is unknown. Here, we combine human electroencephalography with simultaneous intelligibility measurements to address this key gap. We find that the neural envelope-domain signal-to-noise ratio in target-speech encoding, which is shaped by masker modulations, predicts intelligibility over a range of strategically chosen realistic listening conditions unseen by the predictive model. This provides neurophysiological evidence for modulation masking. Moreover, using high-resolution vocoding to carefully control peripheral envelopes, we show that target-envelope coding fidelity in the brain depends not only on envelopes conveyed by the cochlea, but also on the temporal fine structure (TFS), which supports scene segregation. Our results are consistent with the notion that temporal coherence of sound elements across envelopes and/or TFS influences scene analysis and attentive selection of a target sound. Our findings also inform speech-intelligibility models and technologies attempting to improve real-world speech communication.
ISSN:0001-4966
1520-8524
DOI:10.1121/10.0006385