Language Cognition and Pronunciation Training Using Applications

2020 ◽  
Vol 12 (3) ◽  
pp. 42 ◽  
Author(s):  
Ming Sung Kan ◽  
Atsushi Ito
Keyword(s):  
Machine Learning ◽  
Language Learning ◽  
Learning Strategies ◽  
Cognitive Load Theory ◽  
Task Design ◽  
Training Data ◽  
Small Step ◽  
Voice Analysis ◽  
Load Theory ◽  
Perception Training

In language learning, adults seem to be superior in their ability to memorize knowledge of new languages and have better learning strategies, experiences, and intelligence to be able to integrate new knowledge. However, unless one learns pronunciation in childhood, it is almost impossible to reach a native-level accent. In this research, we take the difficulties of learning tonal pronunciation in Mandarin as an example and analyze the difficulties of tone learning and the deficiencies of general learning methods using the cognitive load theory. With the tasks designed commensurate with the learner’s perception ability based on perception experiments and small-step learning, the perception training app is more effective for improving the tone pronunciation ability compared to existing apps with voice analysis function. Furthermore, the learning effect was greatly improved by optimizing the app interface and operation procedures. However, as a result of the combination of pronunciation practice and perception training, pronunciation practice with insufficient feedback could lead to pronunciation errors. Therefore, we also studied pronunciation practice using machine learning and aimed to train the model for the pronunciation task design instead of classification. We used voices designed as training data and trained a model for pronunciation training, and demonstrated that supporting pronunciation practice with machine learning is practicable.

The implications of Cognitive Load Theory and exposure to subtitles in English Foreign Language (EFL)

2018 ◽  
Vol 4 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Anca Daniela Frumuselu
Keyword(s):  
Foreign Language ◽  
Cognitive Load ◽  
Language Learning ◽  
Cognitive Processing ◽  
Cognitive Load Theory ◽  
Cognitive Theory ◽  
Empirical Studies ◽  
Load Theory ◽  
Colloquial Language ◽  
Language Classroom

Abstract The pedagogical use of subtitled and captioned material in the foreign language classroom is upheld by various theories which reveal the cognitive processing activated when students are exposed to multimedia and subtitled audiovisual materials. The three theories that will be considered here are Cognitive Load Theory (CLT), Cognitive Theory of Multimedia Learning (CTML) and Cognitive Affective Theory of Learning with Media (CATLM). The main purpose of the paper is to illustrate the internal mechanisms triggered in learners when various sensorial channels (visual, auditory and textual) coincide simultaneously on screen and how this may affect their cognitive engagement and motivation while learning a foreign language. Additionally, two empirical studies will be presented in the second part of the article in order to provide evidence of the benefits of using subtitled audiovisual materials in the English Foreign Language (EFL) classroom in higher education. The results show that both interlingual (L1) and intralingual (L2) subtitles prove to have a facilitating role in informal and colloquial language learning in this context.

scholarly journals Cognitive Load Theory: An Empirical Study of Anxiety and Task Performance in Language Learning

2017 ◽  
Vol 7 (18) ◽  
Author(s):  
I-Jung Chen ◽  
Chi-Cheng Chang
Keyword(s):  
Foreign Language ◽  
Cognitive Load ◽  
Language Learning ◽  
Cognitive Load Theory ◽  
Rating Scale ◽  
Subjective Rating ◽  
Load Theory ◽  
Language Classroom ◽  
Subjective Rating Scale ◽  
And Task

Introducción. Este estudio explora la relación entre tres variables: carga cognitiva, ansiedad hacia el idioma extranjero (IE) y, rendimiento en las tareas. La carga cognitiva hace referencia a la carga que lleva la memoria de trabajo mientras se realiza una tarea en específico. Los autores mantienen la hipótesis de que la ansiedad resta recursos a la memoria de trabajo, dejando reducida capacidad para las actividades cognitivas, e impidiendo así la eficacia.Método. Los participantes fueron 88 estudiantes matriculados en carreras de cuatro años en una universidad técnica de Taiwán. Estudiantes de filología inglesa fueron excluidos. La Foreign Language Classroom Anxiety Scale [Escala de ansiedad en el aula de idioma extranjero] se utilizó para evaluar sus niveles de ansiedad; la Cognitive Load Subjective Rating Scale [Escala para el baremo subjetivo de la carga cognitiva] se utilizó para medir la carga cognitiva mientras realizaban una tarea de comprensión oral en inglés.Resultados. Los estudiantes con mayor ansiedad hacia el idioma extranjero también incurrían en una mayor carga cognitiva. La ansiedad hacia el IE y la carga cognitiva presentaban una correlación negativa con la comprensión oral.Discusión. Los aprendices que sufren más ansiedad incurren en una mayor carga cognitiva y consiguen peores resultados en sus exámenes. Para favorecer la eficacia del aprendizaje, se recomienda a los docentes que identifiquen las situaciones que provoquen ansiedad y que creen un ambiente de apoyo en el aprendizaje, lo que permitiría que los aprendices dedicasen los recursos de la memoria de trabajo totalmente a las tareas de aprendizaje.

scholarly journals Developmental Language Learning from Human/Humanoid Robot Social Interactions

Robotics ◽  
2013 ◽  
pp. 1328-1353 ◽  
Author(s):  
Artur M. Arsénio
Keyword(s):  
Machine Learning ◽  
Social Context ◽  
Language Acquisition ◽  
Language Learning ◽  
Learning Strategies ◽  
Learning Process ◽  
Computational Models ◽  
Robot Learning ◽  
Psychological Models

This chapter presents work on developmental machine learning strategies applied to robots for language acquisition. The authors focus on learning by scaffolding and emphasize the role of the human caregiver for robot learning. Indeed, language acquisition does not occur in isolation, neither can it be a robot’s “genetic legacy.” Rather, they propose that language is best acquired incrementally, in a social context, through human-robot interactions in which humans guide the robot, as if it were a child, through the learning process. The authors briefly discuss psychological models related to this work and describe and discuss computational models that they implemented for robot language acquisition. The authors aim to introduce robots into our society and treat them as us, using child development as a metaphor for robots’ developmental language learning.

scholarly journals Development of Novel Classifying System to Identify the Right Sense of Audio Conversation in Social Networks using Deep Convolution Neural Network

2019 ◽  
Vol 8 (2S6) ◽  
pp. 522-525
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Learning Strategies ◽  
Human Interaction ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Use Of Social Media ◽  
The Right ◽  
The Impact

Social media has paved a new way for communication and interacting with others. The use of social media differs according to the socio-cultural, demographic and psychological aspects of individuals. People chat, share ideas and visual material, and feel that they satisfy their needs of belonging along with the groups they have joined. Social networks is not only a area of freedom where persons express themselves openly or furtively, but also an area where several ways of violence emerge or even a means used for some aspects of violence.. The present research throws light on a few of the regular and trendy methods of abuse and risks faced by the users of social media. Develop a system to identify abusing audio file by an individual on a people/ group based on common language, race, sexual preferences, religion, or nationality. We examine a new model from machine learning, namely deep machine learning by probing design configurations of deep Convolutional Neural Networks (CNN) and the impact of different hyper-parameter settings in identifying the negative aspects in social media. Deep CNN automatically generate powerful features by hierarchical learning strategies from massive amounts of training data with a minimum of human interaction or expert process knowledge. An application of the proposed method demonstrates excellent results with low false alarm rates for Twitter data

Developmental Language Learning from Human/Humanoid Robot Social Interactions

2013 ◽  
pp. 197-223
Author(s):  
Artur M. Arsénio
Keyword(s):  
Machine Learning ◽  
Social Context ◽  
Language Acquisition ◽  
Language Learning ◽  
Learning Strategies ◽  
Learning Process ◽  
Computational Models ◽  
Robot Learning ◽  
Psychological Models

This chapter presents work on developmental machine learning strategies applied to robots for language acquisition. The authors focus on learning by scaffolding and emphasize the role of the human caregiver for robot learning. Indeed, language acquisition does not occur in isolation, neither can it be a robot’s “genetic legacy.” Rather, they propose that language is best acquired incrementally, in a social context, through human-robot interactions in which humans guide the robot, as if it were a child, through the learning process. The authors briefly discuss psychological models related to this work and describe and discuss computational models that they implemented for robot language acquisition. The authors aim to introduce robots into our society and treat them as us, using child development as a metaphor for robots’ developmental language learning.

scholarly journals A review and experimental analysis of active learning over crowdsourced data

2021 ◽  
Author(s):  
Burcu Sayin ◽  
Evgeny Krivosheev ◽  
Jie Yang ◽  
Andrea Passerini ◽  
Fabio Casati
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategies ◽  
Effective Means ◽  
Training Data ◽  
Learning Approaches ◽  
Data Sampling ◽  
Classification Problems ◽  
Crowdsourced Data ◽  
Hybrid Classification

AbstractTraining data creation is increasingly a key bottleneck for developing machine learning, especially for deep learning systems. Active learning provides a cost-effective means for creating training data by selecting the most informative instances for labeling. Labels in real applications are often collected from crowdsourcing, which engages online crowds for data labeling at scale. Despite the importance of using crowdsourced data in the active learning process, an analysis of how the existing active learning approaches behave over crowdsourced data is currently missing. This paper aims to fill this gap by reviewing the existing active learning approaches and then testing a set of benchmarking ones on crowdsourced datasets. We provide a comprehensive and systematic survey of the recent research on active learning in the hybrid human–machine classification setting, where crowd workers contribute labels (often noisy) to either directly classify data instances or to train machine learning models. We identify three categories of state of the art active learning methods according to whether and how predefined queries employed for data sampling, namely fixed-strategy approaches, dynamic-strategy approaches, and strategy-free approaches. We then conduct an empirical study on their cost-effectiveness, showing that the performance of the existing active learning approaches is affected by many factors in hybrid classification contexts, such as the noise level of data, label fusion technique used, and the specific characteristics of the task. Finally, we discuss challenges and identify potential directions to design active learning strategies for hybrid classification problems.

Assessment of Cognitive Load in Multimedia Learning Using Dual-Task Methodology

2002 ◽  
Vol 49 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Roland Brünken ◽  
Susan Steinbacher ◽  
Jan L. Plass ◽  
Detlev Leutner
Keyword(s):  
Cognitive Load ◽  
Dual Task ◽  
Cognitive Load Theory ◽  
Multimedia Learning ◽  
Learning Systems ◽  
Learning Materials ◽  
New Approach ◽  
Direct Assessment ◽  
Load Theory ◽  
Audiovisual Presentation

Abstract. In two pilot experiments, a new approach for the direct assessment of cognitive load during multimedia learning was tested that uses dual-task methodology. Using this approach, we obtained the same pattern of cognitive load as predicted by cognitive load theory when applied to multimedia learning: The audiovisual presentation of text-based and picture-based learning materials induced less cognitive load than the visual-only presentation of the same material. The findings confirm the utility of dual-task methodology as a promising approach for the assessment of cognitive load induced by complex multimedia learning systems.

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