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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Jong In Kim Sunhee Kim Minhwa Chung Interdisciplinary Program in Cognitive Science Seoul National University Republic of Korea Department of French Language Education Seoul National University Republic of Korea Department of Linguistics Seoul National University Republic of Korea

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

In Computer-Assisted Pronunciation Training (CAPT), accurate phonetic transcriptions are essential for identifying mispronunciations in non-native speech corpora. Publicly available corpora for Korean English L2 learners often lack these transcriptions due to their limited size and availability. To address the shortage of accurate phonetic transcriptions, we propose a method that combines multiple Self-Supervised Learning (SSL)-based phone recognition systems with Recognizer Output Voting Error Reduction (ROVER). We trained SSL-based phone recognizers (Data2vec, Hubert, Wav2vec) on the Librispeech and CommonVoice datasets and used them to decode the L2arctic corpus. By applying ROVER, we achieved 85.5% accuracy in phone transcription compared to manual tagging. Additionally, an error analysis of 140 beginner-level sentences from the Korean Spoken English Corpus (NIA144) identified common pronunciation errors among Korean English speakers.

关键词： Training Accuracy Error analysis Asia Self-supervised learning Manuals Information processing Phonetics Tagging

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Noun2Verb: Probabilistic frame semantics for word class conversion

arXiv

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arXiv 2022年

作者： Yu, Lei Xu, Yang Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Vector Institute for Artificial Intelligence Canada

Humans can flexibly extend word usages across different grammatical classes, a phenomenon known as word class conversion. Noun-to-verb conversion, or denominal verb (e.g., to Google a cheap flight), is one of the most prevalent forms of word class conversion. However, existing natural language processing systems are impoverished in interpreting and generating novel denominal verb usages. Previous work has suggested that novel denominal verb usages are comprehensible if the listener can compute the intended meaning based on shared knowledge with the speaker. Here we explore a computational formalism for this proposal couched in frame semantics. We present a formal framework, Noun2Verb, that simulates the production and comprehension of novel denominal verb usages by modeling shared knowledge of speaker and listener in semantic frames. We evaluate an incremental set of probabilistic models that learn to interpret and generate novel denominal verb usages via paraphrasing. We show that a model where the speaker and listener cooperatively learn the joint distribution over semantic frame elements better explains the empirical denominal verb usages than state-of-the-art language models, evaluated against data from 1) contemporary English in both adult and child speech, 2) contemporary Mandarin Chinese, and 3) the historical development of English. Our work grounds word class conversion in probabilistic frame semantics and bridges the gap between natural language processing systems and humans in lexical creativity. © 2022, CC BY.

关键词： Semantics

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Factors Affecting Interior Acceleration Sound Preference for Electric Vehicles: A Path Analysis

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2022年第1期66卷 1902 - 1907页

作者： May Jorella Lazaro Sungho Kim Adriance Wilfred Anggang Minsik Choi Myung Hwan Yun 1Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Department of Industrial Engineering Seoul National University Seoul Korea

Vehicle sound design is gaining attention in the automotive industry, especially for Electric Vehicles (EV). For EVs, acceleration sound is critical for both user experience (UX) and safety. Despite the abundance of UX-related studies investigating the external presentation of acceleration sound for EVs, internal presentation of acceleration sound seems to be overlooked. Thus, further understanding on what influences the user preferences for internal EV sound is essential for better EV sound design. This study aims to explore and develop a simple theoretical path model to help understand the relationship between pragmatic quality, hedonic quality, novelty, and user preferences for EV internal acceleration sounds. Thirty-two participants evaluated twenty-seven EV acceleration sound samples using a 12-item semantic differential scale with bipolar adjective pairs that describe the measured variables in a controlled experimental setting. The relationship between the modeled variables was analyzed using bias-corrected factor score path analysis (BCFSPA). Results showed that the modified model yielded good model fit indices and partially confirmed the initial hypotheses. It was found that pragmatic and hedonic quality had a positive relationship with user preference, whereas, novelty had a negative relationship with hedonic quality and user preference for EV sounds. This study contributes to the understanding of factors that affects user preference for EV sound and provides initial accounts to different approaches and methods for model testing.

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Aligning Language and Memory Accounts of Semantic Interference 44

Aligning Language and Memory Accounts of Semantic Interferen...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Hambric, Channing O'Séaghdha, Pádraig Department of Psychology Lehigh University 17 Memorial Dr. E BethlehemPA18015 United States Department of Psychology and Cognitive Science Program Lehigh University 17 Memorial Dr. E BethlehemPA18015 United States

Parallel accounts of interference resulting from the generation of related words can be found in the retrieval-induced forgetting (RIF) and the cumulative semantic interference literatures. Recent work on the language production side suggests that the same adaptive learning process may underlie both. However, the literatures remain separate. They use different procedures and dependent measures, and theoretical accounts focus on underlying conceptual representations (memory research) vs. conceptual-lexical links (language research). We propose that the accounts should be reconciled. As an initial step toward this goal we combined a retrieval/generation procedure with a continuous picture-naming test phase to assess their combined effects on interference. We observed both costs and benefits in error data. There were more naming errors (including many timeouts) for non-generated items from activated categories and fewer for previously generated items. Perhaps due to a too-severe cutoff, naming times did not show a RIF influence, only a marginal facilitation effect for generated items. However, naming time showed typical cumulative interference within the picture-naming phase independent of previous retrieval experience. Future work will investigate the locus of interference in conceptual memory representations versus in links to word representations with the goal of producing a unified account of semantic interference. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： cued recall incremental learning retrieval-induced forgetting semantic interference word production

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Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning 7

Improving Candle Direction Classification in Forex Market Us...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Oetama, Raymond Sunardi Heryadi, Yaya Lukas Suparta, Wayan Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned. © 2022 IEEE.

关键词： Support vector machines

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The Effect of Major Depressive Disorder on Producing Intelligible Speech

SSRN

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SSRN 2024年

作者： Seneviratne, Nadee Li, Leslie Espy-Wilson, Carol Department of Electrical and Computer Engineering University of Maryland College ParkMD20740 United States Program in Neuroscience and Cognitive Science Department of Linguistics University of Maryland College ParkMD20740 United States

Understanding the characteristics of depressed speech can provide insights on aspects to consider when developing biomarkers for automatic depression detection systems. We present the results of a speech intelligibility study that was conducted to assess the intelligibility of the speech of a female and a male subject when they were severely depressed and when they were in remission. Our analysis shows that, in general, the depressed speech is at least as intelligible as in-remission speech. This is further supported by the underlying "simpler" articulatory coordination of depressed speech as derived from the Articulatory Coordination Features. Acoustically, the simpler articulatory coordination means that there is less variability due to coarticulation in the speech signal. © 2024, The Authors. All rights reserved.

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How much to copy from others? The role of partial copying in social learning 42

How much to copy from others? The role of partial copying in...

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42nd Annual Meeting of the cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Campbell, Chelsea M. Izquierdo, Eduardo J. Goldstone, Robert L. Cognitive Science Program Indiana University BloomingtonIN47406 United States

One of the major ways that people engage in adaptive problem solving is by copying the solutions of others. Most of the work on this field has focused on three questions: when to copy, who to copy from, and what to copy. However, how much to copy has been relatively less explored. In the current research, we are interested in the consequences for a group when its members engage in social learning strategies with different tendencies to copy entire or partial solutions and different complexities of search problems. We also consider different network topologies that affect the solutions visible to each member. Using a computational model of collective problem solving, we demonstrate that strategies where social learning involves partial copying outperform strategies where individuals copy entire solutions. We analyze the exploration/exploitation dynamics of these social learning strategies under the different conditions. © 2020 The Author(s)

关键词： Network topology

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Universal hydrogel adhesives with robust chain entanglement for bridging soft electronic materials

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npj Flexible Electronics 2024年第1期8卷 509-522页

作者： Yejin Jo Yurim Lee Jeong Hyun Heo Yeonzu Son Tae Young Kim Kijun Park Soye Kim Seo Jung Kim Yoonhee Jin Seongjun Park Jungmok Seo School of Electrical and Electronic Engineering Yonsei UniversitySeoul03722Republic of Korea Department of Physiology Yonsei University College of MedicineSeoul03722Republic of Korea Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon34141Republic of Korea Department of Pediatrics Severance Children’s HospitalYonsei University College of MedicineSeoul03722Republic of Korea Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon34141Republic of Korea Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-roYuseong-guDaejeon34141Republic of Korea KAIST Institute for Nano Century(KINC) 291 Daehak-roYuseong-guDaejeon34141Republic of Korea

Ensuring stable integration of diverse soft electronic components for reliable operation under dynamic conditions is ***,integrating soft electronics,comprising various materials like polymers,metals,and hydrogels,poses challenges due to their different mechanical and chemical *** study introduces a dried-hydrogel adhesive made of poly(vinyl alcohol)and tannic acid multilayers(d-HAPT),which integrates soft electronic materials through moisture-derived chain entanglement.d-HAPT is a thin(~1μm)and highly transparent(over 85%transmittance in the visible light region)adhesive,showing robust bonding(up to 3.6 MPa)within a short time(<1 min).d-HAPT demonstrates practical application in wearable devices,including a hydrogel touch panel and strain ***,the potential of d-HAPT for use in implantable electronics is demonstrated through in vivo neuromodulation and electrocardiographic recording experiments while confirming its biocompatibility both in vitro and in *** is expected that d-HAPT will provide a reliable platform for integrating soft electronic applications.

关键词： properties hydrogel electronic

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3D-to-2D-to-3D Conscious Learning

3D-to-2D-to-3D Conscious Learning

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Weng, Juyang Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Cognitive Science Program Michigan State University East LansingMI48824 United States Neuroscience Program Michigan State University East LansingMI48824 United States Brain-Mind Institute OkemosMI48864 United States GENISAMA OkemosMI48864 United States

ISBN: (纸本)9781665441544

This is a theoretical paper on conscious learning for thoughts and creativity through general-purpose and autonomous imitation of demonstrations. This conscious learning is end-to-end (3D-to-2D-to-3D) and free from annotations of 2D images and 2D motor images (e.g., a bounding box to be attended to). The conscious learning algorithm directly takes that of the Developmental Networks that has been previously published extensively with rich experimental results. Apparently, humans and animals do this type of fully automated learning daily, but it is unclear a robot can do the same. Recently, [1], [2] presented a theory of conscious learning rooted in emergent universal Turing machines. It appeared to be the first algorithmic level theory of holistic consciousness, other than many papers in the literature about piecemeal consciousness. However, [1], [2] proved only conscious learning in motor-imposed training mode, namely 3D-to-2D taught by 2D motor impositions, free from 2D annotations. This paper fills the challenging gap in [1], [2] so the conscious learning is 3D-to-2D-to-3D (end-to-end) without motor-impositions or computing 'inverse kinematics'. This is a major departure from traditional AI-handcrafting symbolic labels that tend to be brittle (e.g., for driverless cars) and then 'spoon-feeding' pre-collected 'big data'. Autonomous imitations drastically reduce the teaching complexity compared to pre-collected 'big data', especially because no annotations of training data are needed. Furthermore, conscious learning allows creativity beyond what is taught. This work is directly related to consumer electronics because it requires large-scale on-the-fly brainoid chips in future wearable robots/devices for consumers. © 2022 IEEE.

关键词： Big data

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Analysis of Various Self-Supervised Learning Models for Automatic Pronunciation Assessment

Analysis of Various Self-Supervised Learning Models for Auto...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Haeyoung Lee Sunhee Kim Minhwa Chung Interdisciplinary Program in Cognitive Science Seoul National University Seoul Republic of Korea Department of French Language Education Seoul National University Seoul Republic of Korea Department of Linguistics Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

The advancement of Automatic Pronunciation Assessment (APA) systems has been significantly improved by Self-supervised Learning (SSL) models. However, despite these performance gains, there remains a lack of systematic research on effective utilization of SSL models and the explainability of their behavior in APA. This study aims to evaluate pronunciation with high accuracy using SSL models and to provide explanations for the scoring outcomes. To achieve this, we fine-tune various SSL models using multiple strategies, comparing their performance through extrinsic analysis to identify the key factors influencing performance improvements. Furthermore, intrinsic analysis is conducted using Principal Component Analysis (PCA) to gain insights into the model’s scoring patterns. Extrinsic analysis highlights the importance of strategic fine-tuning and acoustic similarity between fine-tuning and pre-training datasets. Intrinsic analysis reveals that different SSL models focus on distinct pronunciation features, with the Wav2Vec2.0 model capturing more advantageous information for APA. This study presents the first in-depth analysis of SSL models in APA, proposing a novel intrinsic analysis method based on feature distribution manifolds. We provide model-specific fine-tuning guidelines for APA tasks and recommend appropriate SSL models based on specific pronunciation assessment goals. This research significantly contributes to the future development of explainable APA systems based on SSL models.

关键词： Manifolds Analytical models Systematics Shape Self-supervised learning Information processing Performance gain Vectors Principal component analysis Guidelines

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