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Differences in counting skills between Chinese and German children are accompanied by differences in processing of approximate numerical magnitude information
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In: Frontiers in psychology 9 (2019), 8 S. (2019)
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| 2 |
Differences in Counting Skills Between Chinese and German Children Are Accompanied by Differences in Processing of Approximate Numerical Magnitude Information
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| 3 |
Differences in Counting Skills Between Chinese and German Children Are Accompanied by Differences in Processing of Approximate Numerical Magnitude Information
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The Efficacy of Written Corrective Feedback on Second Language Development: The Impact of Feedback Type, Revision Type, Learning Motivation and Strategies
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Li, Su. - : Auckland University of Technology, 2017
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Do Preschool Children Learn to Read Words from Environmental Prints?
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| 6 |
Fine neural tuning for orthographic properties of words emerges early in children reading alphabetic script
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In: Zhao, Jing; Kipp, Kerstin; Gaspar, Carl; Maurer, Urs; Weng, Xuchu; Mecklinger, Axel; Li, Su (2014). Fine neural tuning for orthographic properties of words emerges early in children reading alphabetic script. Journal of Cognitive Neuroscience, 26(11):2431-2442. (2014)
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Vocabulary Learning Beliefs, Strategies and Language Learning Outcomes: a study of Chinese learners of English in Higher Vocational Education
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Li, Su. - : Auckland University of Technology, 2010
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| 8 |
Cross-lingual Conversion of Lexical Semantic Relations: Building Parallel Wordnets
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In: http://cwn.ling.sinica.edu.tw/churen/ALR05_bwn.pdf
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Durative Event: A Comparison of 趕 gan3 and 搶 qiang3
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In: http://cwn.ling.sinica.edu.tw/churen/qiang_gan.pdf
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| 10 |
Paranyms, Co-Hyponyms and Antonyms: Representing Semantic Fields with Lexical Semantic Relations
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In: http://cwn.ling.sinica.edu.tw/churen/2007 paranyms co-hyponyms and antonyms.pdf
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| 11 |
processing: A
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In: http://cwn.ling.sinica.edu.tw/churen/2007iwic-swadesh-final.pdf
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Meta-modeling and standardization issues for Asian Languages lexical resources
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In: http://cwn.ling.sinica.edu.tw/churen/meta-modeling and standardization issues.pdf
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| 13 |
Cross-lingual Conversion of Lexical Semantic Relations: Building Parallel Wordnets
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In: http://wing.comp.nus.edu.sg/~antho/I/I05/I05-4007.pdf
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Basic Lexicon and Shared Ontology for Multilingual Resources: A SUMO+MILO Hybrid Approach
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In: http://shukai.googlepages.com/ontolex07-camera.pdf
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| 15 |
Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem
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Abstract:
There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.Author summary: The ability to understand spoken language is a defining human capacity. But despite decades of research, there is still no well-specified account of how sound entering the ear is neurally interpreted as a sequence of meaningful words. At the same time, modern computer-based Automatic Speech Recognition (ASR) systems are capable of near-human levels of performance, especially where word-identification is concerned. In this research we aim to bridge the gap between human and machine solutions to speech recognition. We use a novel combination of neuroimaging and statistical methods to relate human and machine internal states that are dynamically generated as spoken words are heard by human listeners and analysed by ASR systems. We find that the stable regularities discovered by the ASR process, linking speech input to phonetic labels, can be significantly related to the regularities extracted in the human brain. Both systems may have in common a representation of these regularities in terms of articulatory phonetic features, consistent with an analysis process which recovers the articulatory gestures that generated the speech. These results suggest a possible partnership between human- and machine-based research which may deliver both a better understanding of how the human brain provides such a robust solution to speech understanding, and generate insights that enhance the performance of future ASR systems.
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URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005617
https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005617&type=printable
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