ArticleLanguage, cognition and neuroscience2023
LDL-AURIS: a computational model, grounded in error-driven learning, for the comprehension of single spoken words.
Article in Language, cognition and neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- The Perception of Novel Consonant Clusters: A Comparison of Salience and Sonority.Brain sciences · 2026Article
- The pluralization palette: unveiling semantic clusters in English nominal pluralization through distributional semantics.Morphology (Dordrecht, Netherlands) · 2024Article
- How trial-to-trial learning shapes mappings in the mental lexicon: Modelling lexical decision with linear discriminative learning.Cognitive psychology · 2023Article
- Frequency effects in linear discriminative learning.Frontiers in human neuroscience · 2023Article
- DIANA, a Process-Oriented Model of Human Auditory Word Recognition.Brain sciences · 2022Article
- Morpho-Phonetic Effects in Speech Production: Modeling the Acoustic Duration of English Derived Words With Linear Discriminative Learning.Frontiers in psychology · 2021Article
- Modeling Morphology With Linear Discriminative Learning: Considerations and Design Choices.Frontiers in psychology · 2021Article
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4 authors.
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Abstract
A computational model for the comprehension of single spoken words is presented that builds on an earlier model using discriminative learning. Real-valued features are extracted from the speech signal instead of discrete features. Vectors representing word meanings using one-hot encoding are replaced by real-valued semantic vectors. Instead of incremental learning with Rescorla-Wagner updating, we use linear discriminative learning, which captures incremental learning at the limit of experience. These new design features substantially improve prediction accuracy for unseen words, and provide enhanced temporal granularity, enabling the modelling of cohort-like effects. Visualisation with t-SNE shows that the acoustic form space captures phone-like properties. Trained on 9 h of audio from a broadcast news corpus, the model achieves recognition performance that approximates the lower bound of human accuracy in isolated word recognition tasks. LDL-AURIS thus provides a mathematically-simple yet powerful characterisation of the comprehension of single words as found in English spontaneous speech.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.