Distinct neural encoding of glimpsed and masked speech in multitalker situations

Abstract

Humans can easily tune in to one talker in a multitalker environment while still picking up bits of background speech; however, it remains unclear how we perceive speech that is masked and to what degree non-target speech is processed. Some models suggest that perception can be achieved through glimpses, which are spectrotemporal regions where a talker has more energy than the background. Other models, however, require the recovery of the masked regions. To clarify this issue, we directly recorded from primary and non-primary auditory cortex (AC) in neurosurgical patients as they attended to one talker in multitalker speech and trained temporal response function models to predict high-gamma neural activity from glimpsed and masked stimulus features. We found that glimpsed speech is encoded at the level of phonetic features for target and non-target talkers, with enhanced encoding of target speech in non-primary AC. In contrast, encoding of masked phonetic features was found only for the target, with a greater response latency and distinct anatomical organization compared to glimpsed phonetic features. These findings suggest separate mechanisms for encoding glimpsed and masked speech and provide neural evidence for the glimpsing model of speech perception.

Neural correlation of speech envelope tracking for background noise in normal hearing

“…Consequently, the higher-performance group likely expended less listening effort to process speech with informational masking, such as story noise, which could explain their lower envelope tracking coefficients. Notably, recent research by Raghavan et al (2023) has proposed distinct mechanisms for encoding glimpsed and masked speech, providing neural evidence that supports the glimpsing model of speech perception.…”

naplib-python: Neural acoustic data processing and analysis tools in python

“…Introducing this package will help reduce the overhead of writing new preprocessing and analysis code, especially for researchers with minimal computational experience, and improve the reproducibility of research by making code-sharing easier. The package was critical in a recent study of noise adaptation mechanisms [ 35 ], where it was used for phoneme- and word-alignment, segmentation, statistical testing, and visualization, as well as a study of multitalker speech encoding [ 36 ], where it was used to extract a wide variety of stimulus features which were used for fitting models to predict neural responses. In both these studies, the package aided in the data analysis stage by providing the computational framework for various feature extraction and testing methods.…”

Attention to audiovisual speech shapes neural processing through feedback-feedforward loops between different nodes of the speech network

“…Recent methodological advances in electrocorticography (ECoG) [ 15 , 24 – 27 ], magnetoencephalography (MEG) [ 11 , 28 ], and electroencephalography (EEG) [ 25 , 29 , 30 ] have revealed that attention enhances neuronal tracking of speech sounds. This amplification is concordant with modulation of both early (i.e., within 100 ms; e.g., [ 31 ]) and late (after 100 ms; e.g., [ 32 , 33 ]) neural response curves to sound envelope changes, consistent with the view that selective attention shifts neuronal processing in low-level auditory and higher-level speech-sensitive regions towards the features of the attended speaker [ 24 , 31 , 34 , 35 ].…”