An official website of the United States government
Abstract Objective: Acoustic distortions to the speech signal impair spoken language recognition, but healthy listeners exhibit adaptive plasticity consistent with rapid adjustments in how the distorted speech input maps to speech representations, perhaps through engagement of supervised error-driven learning. This puts adaptive plasticity in speech perception in an interesting position with regard to developmental dyslexia inasmuch as dyslexia impacts speech processing and may involve dysfunction in neurobiological systems hypothesized to be involved in adaptive plasticity. Method: Here, we examined typical young adult listeners ( N = 17), and those with dyslexia ( N = 16), as they reported the identity of native-language monosyllabic spoken words to which signal processing had been applied to create a systematic acoustic distortion. During training, all participants experienced incremental signal distortion increases to mildly distorted speech along with orthographic and auditory feedback indicating word identity following response across a brief, 250-trial training block. During pretest and posttest phases, no feedback was provided to participants. Results: Word recognition across severely distorted speech was poor at pretest and equivalent across groups. Training led to improved word recognition for the most severely distorted speech at posttest, with evidence that adaptive plasticity generalized to support recognition of new tokens not previously experienced under distortion. However, training-related recognition gains for listeners with dyslexia were significantly less robust than for control listeners. Conclusions: Less efficient adaptive plasticity to speech distortions may impact the ability of individuals with dyslexia to deal with variability arising from sources like acoustic noise and foreign-accented speech.
more »
« less
Spoken Word Recognition: A Focus on Plasticity
Psycholinguists define spoken word recognition (SWR) as, roughly, the processes intervening between speech perception and sentence processing, whereby a sequence of speech elements is mapped to a phonological wordform. After reviewing points of consensus and contention in SWR, we turn to the focus of this review: considering the limitations of theoretical views that implicitly assume an idealized (neurotypical, monolingual adult) and static perceiver. In contrast to this assumption, we review evidence that SWR is plastic throughout the life span and changes as a function of cognitive and sensory changes, modulated by the language(s) someone knows. In highlighting instances of plasticity at multiple timescales, we are confronted with the question of whether these effects reflect changes in content or in processes, and we consider the possibility that the two are inseparable. We close with a brief discussion of the challenges that plasticity poses for developing comprehensive theories of spoken language processing.
more »
« less
- Award ID(s):
- 2043903
- PAR ID:
- 10509559
- Publisher / Repository:
- Annual Reviews
- Date Published:
- Journal Name:
- Annual Review of Linguistics
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2333-9683
- Page Range / eLocation ID:
- 233-256
- Subject(s) / Keyword(s):
- spoken word recognition plasticity development multilingualism training adaptation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract To understand human language—both spoken and signed—the listener or viewer has to parse the continuous external signal into components. The question of what those components are (e.g., phrases, words, sounds, phonemes?) has been a subject of long‐standing debate. We re‐frame this question to ask: What properties of the incoming visual or auditory signal are indispensable to eliciting language comprehension? In this review, we assess the phenomenon of language parsing from modality‐independent viewpoint. We show that the interplay between dynamic changes in the entropy of the signal and between neural entrainment to the signal at syllable level (4–5 Hz range) is causally related to language comprehension in both speech and sign language. This modality‐independent Entropy Syllable Parsing model for the linguistic signal offers insight into the mechanisms of language processing, suggesting common neurocomputational bases for syllables in speech and sign language. This article is categorized under:Linguistics > Linguistic TheoryLinguistics > Language in Mind and BrainLinguistics > Computational Models of LanguagePsychology > Languagemore » « less
-
Abstract Sign languages are human communication systems that are equivalent to spoken language in their capacity for information transfer, but which use a dynamic visual signal for communication. Thus, linguistic metrics of complexity, which are typically developed for linear, symbolic linguistic representation (such as written forms of spoken languages) do not translate easily into sign language analysis. A comparison of physical signal metrics, on the other hand, is complicated by the higher dimensionality (spatial and temporal) of the sign language signal as compared to a speech signal (solely temporal). Here, we review a variety of approaches to operationalizing sign language complexity based on linguistic and physical data, and identify the approaches that allow for high fidelity modeling of the data in the visual domain, while capturing linguistically-relevant features of the sign language signal.more » « less
-
Grounded language acquisition is a major area of research combining aspects of natural language processing, computer vision, and signal processing, compounded by domain issues requiring sample efficiency and other deployment constraints. In this work, we present a multimodal dataset of RGB+depth objects with spoken as well as textual descriptions. We analyze the differences between the two types of descriptive language and our experiments demonstrate that the different modalities affect learning. This will enable researchers studying the intersection of robotics, NLP, and HCI to better investigate how the multiple modalities of image, depth, text, speech, and transcription interact, as well as how differences in the vernacular of these modalities impact results.more » « less
-
Grounded language acquisition is a major area of research combining aspects of natural language processing, computer vision, and signal processing, compounded by domain issues requiring sample efficiency and other deployment constraints. In this work, we present a multimodal dataset of RGB+depth objects with spoken as well as textual descriptions. We analyze the differences between the two types of descriptive language and our experiments demonstrate that the different modalities affect learning. This will enable researchers studying the intersection of robotics, NLP, and HCI to better investigate how the multiple modalities of image, depth, text, speech, and transcription interact, as well as how differences in the vernacular of these modalities impact results.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-linguistics-031422-113507
