The Frame of Cognitive Pattern Recognition

Pi, Yiming; Shu, Huailin; Tiancai, Liang

doi:10.1109/chicc.2006.4347565

Cited by 10 publications

(2 citation statements)

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“…The idea of an external environment of speakers linguistically interacting in sometimes imperfect ways was considered a 'theory of everything' (Chomsky 2000) and as such a scientific nonstarter. Conventions, 42 For those worried about acquisition and poverty of stimulus issues here, I refer them to the growing literature on neural networks and pattern recognition (Youguo et al 2007;Mattson 2014). These are learning models completely based on empiricist mechanisms and sometimes minimal inductive biases programmed in and represent yet another way in which complexity science can illuminate linguistic phenomena and processes such as language acquisition.…”

Section: Systems Biolinguisticsmentioning

confidence: 99%

Biolinguistics and biological systems: a complex systems analysis of language

Nefdt

2023

Biol Philos

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In their recent book, Ladyman and Wiesner (What is a complex system?, Yale University Press, 2020) delineate the bounds of the exciting interdisciplinary field of complexity science. In this work, they provide examples of generally accepted complex systems and common features which these possess to varying degrees. In this paper, I plan to extend their list to include the formal study of natural language, i.e. linguistics. In fact, I will argue that language exhibits many of the hallmarks of a complex system, specifically a complex biological system. Thus, my aim is to advocate contra the the ‘Minimalist Program’ (Chomsky, The minimalist program, MIT Press, Cambridge, 1995), which motivates simple underlying mechanisms (i.e. Merge) in their idealisations, that biolinguistics should embrace a ‘Maximalist Program’ in which multiple subfields contribute component explanations to an emerging whole.

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Section: Systems Biolinguisticsmentioning

confidence: 99%

Biolinguistics and biological systems: a complex systems analysis of language

Nefdt

2023

Biol Philos

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“…Computing the Euclidean Distance (ED) is a key part in many machine learning and template matching methods to find the closest members of the training set as well as to estimate the possibly feature vector. A speech recognition is the one of the pattern recognition support system that has been so much developed [2]- [4]. Feature extraction become a challenges in creating a good speech recognition systems [3].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Based Feature Extraction for The Indonesian CV Syllables Sound

2018

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This paper proposes the comb ined methods of Wavelet Transform (WT) and Euclidean Distance (ED) to estimate the expected value of the possib ly feature vector of Indonesian syllab les. This research aims to find the b est properties in effectiveness and efficiency on performing feature extraction of each syllab le sound to b e applied in the speech recognition systems. This proposed approach which is the state-of-the-art of the previous study consist of three main phase. In the first phase, the speech signal is segmented and normalized. In the second phase, the signal is transformed into frequency domain b y using the WT. In the third phase, to estimate the expected feature vector, the ED algorithm is used. Th e result shows the list of features of each syllab les can b e used for the next research, and some recommendations on the most effective and efficient WT to b e used in performing syllab le sound recognition.

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