Spectral degradation reduces access to the acoustics of spoken language and compromises how learners break into its structure. We hypothesised that spectral degradation disrupts word segmentation, but that listeners can exploit other cues to restore detection of words. Normal-hearing adults were familiarised to artificial speech that was unprocessed or spectrally degraded by noise-band vocoding into 16 or 8 spectral channels. The monotonic speech stream was pause-free (Experiment 1), interspersed with isolated words (Experiment 2), or slowed by 33% (Experiment 3). Participants were tested on segmentation of familiar vs. novel syllable sequences and on recognition of individual syllables. As expected, vocoding hindered both word segmentation and syllable recognition. The addition of isolated words, but not slowed speech, improved segmentation. We conclude that syllable recognition is necessary but not sufficient for successful word segmentation, and that isolated words can facilitate listeners' access to the structure of acoustically degraded speech.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6028043 | PMC |
| http://dx.doi.org/10.1080/23273798.2017.1354129 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Early subacute frontal callosal microstructure and language outcomes after stroke.
Brain Commun
January 2025
Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia.
The integrity of the frontal segment of the corpus callosum, forceps minor, is particularly susceptible to age-related degradation and has been associated with cognitive outcomes in both healthy and pathological ageing. The predictive relevance of forceps minor integrity in relation to cognitive outcomes following a stroke remains unexplored. Our goal was to evaluate whether the heterogeneity of forceps minor integrity, assessed early after stroke onset (2-6 weeks), contributes to explaining variance in longitudinal outcomes in post-stroke aphasia.
View Article and Find Full Text PDFThe influences of social support expressed from doctors and disclosed from peers on patient decision-making: an analysis from the online health community.
Sci Rep
January 2025
School of Management, Hainan Medical University, Haikou, China.
Patients are turning to OHCs to deal with the stresses and complications of infertility. As a stigmatized disease, infertile patients may value informational support and emotional support differently, which is ignored in existing studies. Based on social support theory, this study aims to reveal the role of informational support and emotional support of doctors in infertile patient decision-making.
View Article and Find Full Text PDFIntroduction: Apraxia of speech (AOS) is a motor speech disorder characterized by sound distortions, substitutions, deletions, and additions; slow speech rate; abnormal prosody; and/or segmentation between words and syllables. AOS can result from neurodegeneration, in which case it can be accompanied by the primary agrammatic aphasia (PAA), which when presenting together are called AOS+PAA. AOS can also be the sole manifestation of neurodegeneration, termed primary progressive AOS (PPAOS).
View Article and Find Full Text PDFTemporal logic inference for interpretable fault diagnosis of bearings via sparse and structured neural attention.
ISA Trans
January 2025
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address:
This paper addresses the critical challenge of interpretability in machine learning methods for machine fault diagnosis by introducing a novel ad hoc interpretable neural network structure called Sparse Temporal Logic Network (STLN). STLN conceptualizes network neurons as logical propositions and constructs formal connections between them using specified logical operators, which can be articulated and understood as a formal language called Weighted Signal Temporal Logic. The network includes a basic word network using wavelet kernels to extract intelligible features, a transformer encoder with sparse and structured neural attention to locate informative signal segments relevant to decision-making, and a logic network to synthesize a coherent language for fault explanation.
View Article and Find Full Text PDFClinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.
Sci Rep
January 2025
Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans using both retrospective and prospective data. We used a modified Progressive Concept Bottleneck Model with pre-established clinical concepts as explanations (feedback on image optimization and presence of anatomical landmarks) as well as segmentations (outlining anatomical landmarks).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!