Functional changes in inter- and intra-hemispheric cortical processing underlying degraded speech perception

NeuroImage ◽  
2016 ◽  
Vol 124 ◽  
pp. 581-590 ◽  
Author(s):  
Gavin M. Bidelman ◽  
Megan Howell
2021 ◽  
Author(s):  
Shannon L.M. Heald ◽  
Stephen C. Van Hedger ◽  
John Veillette ◽  
Katherine Reis ◽  
Joel S. Snyder ◽  
...  

AbstractThe ability to generalize rapidly across specific experiences is vital for robust recognition of new patterns, especially in speech perception considering acoustic-phonetic pattern variability. Behavioral research has demonstrated that listeners are rapidly able to generalize their experience with a talker’s speech and quickly improve understanding of a difficult-to-understand talker without prolonged practice, e.g., even after a single training session. Here, we examine the differences in neural responses to generalized versus rote learning in auditory cortical processing by training listeners to understand a novel synthetic talker using a Pretest-Posttest design with electroencephalography (EEG). Participants were trained using either (1) a large inventory of words where no words repeated across the experiment (generalized learning) or (2) a small inventory of words where words repeated (rote learning). Analysis of long-latency auditory evoked potentials at Pretest and Posttest revealed that while rote and generalized learning both produce rapid changes in auditory processing, the nature of these changes differed. In the context of adapting to a talker, generalized learning is marked by an amplitude reduction in the N1-P2 complex and by the presence of a late-negative (LN) wave in the auditory evoked potential following training. Rote learning, however, is marked only by temporally later source configuration changes. The early N1-P2 change, found only for generalized learning, suggests that generalized learning relies on the attentional system to reorganize the way acoustic features are selectively processed. This change in relatively early sensory processing (i.e. during the first 250ms) is consistent with an active processing account of speech perception, which proposes that the ability to rapidly adjust to the specific vocal characteristics of a new talker (for which rote learning is rare) relies on attentional mechanisms to adaptively tune early auditory processing sensitivity.Statement of SignificancePrevious research on perceptual learning has typically examined neural responses during rote learning: training and testing is carried out with the same stimuli. As a result, it is not clear that findings from these studies can explain learning that generalizes to novel patterns, which is critical in speech perception. Are neural responses to generalized learning in auditory processing different from neural responses to rote learning? Results indicate rote learning of a particular talker’s speech involves brain regions focused on the memory encoding and retrieving of specific learned patterns, whereas generalized learning involves brain regions involved in reorganizing attention during early sensory processing. In learning speech from a novel talker, only generalized learning is marked by changes in the N1-P2 complex (reflective of secondary auditory cortical processing). The results are consistent with the view that robust speech perception relies on the fast adjustment of attention mechanisms to adaptively tune auditory sensitivity to cope with acoustic variability.


2013 ◽  
Vol 134 (5) ◽  
pp. 4234-4234 ◽  
Author(s):  
Yang Zhang ◽  
Bing Cheng ◽  
Tess Koerner ◽  
Christine Cao ◽  
Edward Carney ◽  
...  

2019 ◽  
Vol 62 (9) ◽  
pp. 3290-3301
Author(s):  
Jingjing Guan ◽  
Chang Liu

Purpose Degraded speech intelligibility in background noise is a common complaint of listeners with hearing loss. The purpose of the current study is to explore whether 2nd formant (F2) enhancement improves speech perception in noise for older listeners with hearing impairment (HI) and normal hearing (NH). Method Target words (e.g., color and digit) were selected and presented based on the paradigm of the coordinate response measure corpus. Speech recognition thresholds with original and F2-enhanced speech in 2- and 6-talker babble were examined for older listeners with NH and HI. Results The thresholds for both the NH and HI groups improved for enhanced speech signals primarily in 2-talker babble, but not in 6-talker babble. The F2 enhancement benefits did not correlate significantly with listeners' age and their average hearing thresholds in most listening conditions. However, speech intelligibility index values increased significantly with F2 enhancement in babble for listeners with HI, but not for NH listeners. Conclusions Speech sounds with F2 enhancement may improve listeners' speech perception in 2-talker babble, possibly due to a greater amount of speech information available in temporally modulated noise or a better capacity to separate speech signals from background babble.


Author(s):  
Jiaqiang Zhu ◽  
Xiaoxiang Chen ◽  
Fei Chen ◽  
Seth Wiener

Purpose: Individuals with congenital amusia exhibit degraded speech perception. This study examined whether adult Chinese Mandarin listeners with amusia were still able to extract the statistical regularities of Mandarin speech sounds, despite their degraded speech perception. Method: Using the gating paradigm with monosyllabic syllable–tone words, we tested 19 Mandarin-speaking amusics and 19 musically intact controls. Listeners heard increasingly longer fragments of the acoustic signal across eight duration-blocked gates. The stimuli varied in syllable token frequency and syllable–tone co-occurrence probability. The correct syllable–tone word, correct syllable-only, correct tone-only, and correct syllable–incorrect tone responses were compared respectively between the two groups using mixed-effects models. Results: Amusics were less accurate than controls in terms of the correct word, correct syllable-only, and correct tone-only responses. Amusics, however, showed consistent patterns of top-down processing, as indicated by more accurate responses to high-frequency syllables, high-probability tones, and tone errors all in manners similar to those of the control listeners. Conclusions: Amusics are able to learn syllable and tone statistical regularities from the language input. This extends previous work by showing that amusics can track phonological segment and pitch cues despite their degraded speech perception. The observed speech deficits in amusics are therefore not due to an abnormal statistical learning mechanism. These results support rehabilitation programs aimed at improving amusics' sensitivity to pitch.


eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Ediz Sohoglu ◽  
Matthew H Davis

Human speech perception can be described as Bayesian perceptual inference but how are these Bayesian computations instantiated neurally? We used magnetoencephalographic recordings of brain responses to degraded spoken words and experimentally manipulated signal quality and prior knowledge. We first demonstrate that spectrotemporal modulations in speech are more strongly represented in neural responses than alternative speech representations (e.g. spectrogram or articulatory features). Critically, we found an interaction between speech signal quality and expectations from prior written text on the quality of neural representations; increased signal quality enhanced neural representations of speech that mismatched with prior expectations, but led to greater suppression of speech that matched prior expectations. This interaction is a unique neural signature of prediction error computations and is apparent in neural responses within 100 ms of speech input. Our findings contribute to the detailed specification of a computational model of speech perception based on predictive coding frameworks.


NeuroImage ◽  
2012 ◽  
Vol 60 (2) ◽  
pp. 1036-1045 ◽  
Author(s):  
Ismo Miettinen ◽  
Paavo Alku ◽  
Santeri Yrttiaho ◽  
Patrick J.C. May ◽  
Hannu Tiitinen

2019 ◽  
Author(s):  
Matthew H. Davis ◽  
Ediz Sohoglu

Spoken language is one of the most important sounds that humans hear, yet, also one of the most difficult sounds for non-human listeners or machines to identify. In this chapter we explore different neuro-computational implementations of Bayesian Inference for Speech Perception. We propose, in line with Predictive Coding (PC) principles, that Bayesian Inference is based on neural computations of the difference between heard and expected speech segments (Prediction Error). We will review three functions of these Prediction Error representations: (1) in combining prior knowledge and degraded speech for optimal word identification, (2) supporting rapid learning processes so that perception remains optimal despite perceptual degradation or variation, (3) ensuring that listeners detect instances of lexical novelty (previously unfamiliar words) so as to learn new words over the life span. Evidence from MEG and multivariate fMRI studies suggestion computations of Prediction Error in the Superior Temporal Gyrus (STG) during these three processes.


2005 ◽  
Vol 382 (3) ◽  
pp. 254-258 ◽  
Author(s):  
Tetsuaki Kawase ◽  
Keiichiro Yamaguchi ◽  
Takenori Ogawa ◽  
Ken-ichi Suzuki ◽  
Maki Suzuki ◽  
...  

Author(s):  
Derek M. Houston ◽  
Chi-hsin Chen ◽  
Claire Monroy ◽  
Irina Castellanos

It is generally assumed that deaf and hard-of-hearing children’s difficulties in learning novel words stem entirely from impaired speech perception. Degraded speech perception makes words more confusable, and correctly recognizing words clearly plays an important role in word learning. However, recent findings suggest that early auditory experience may affect other factors involved in linking the sound patterns of words to their referents. This chapter reviews those findings and discusses possible factors that may be affected by early auditory experience and, in turn, also affect the ability to learn word-referent associations. These factors include forming representations for the sound patterns of words, encoding phonological information into memory, sensory integration, and quality of language input. Overall, we learn that in order to understand and to help mitigate the difficulties deaf and hard-of-hearing children face in learning spoken words after cochlear implantation, we must look well beyond speech perception.


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