Yao-Ting Sung

Also published as: Yao-Ting Hung


2024

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An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution
Tien-Hong Lo | Fu-An Chao | Tzu-i Wu | Yao-Ting Sung | Berlin Chen
Findings of the Association for Computational Linguistics: NAACL 2024

Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner’s speech. Recently, self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenges: limited annotated data, uneven distribution of learner proficiency levels and non-uniform score intervals between different CEFR proficiency levels. To address these challenges, we explore the use of two novel modeling strategies: metric-based classification and loss re-weighting, leveraging distinct SSL-based embedding features. Extensive experimental results on the ICNALE benchmark dataset suggest that our approach can outperform existing strong baselines by a sizable margin, achieving a significant improvement of more than 10% in CEFR prediction accuracy.

2023

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Impact of Feature Selection Algorithms on Readability Model
Tsai-Ning Tai | Hou-Chiang Tseng | Yao-Ting Sung
Proceedings of the 35th Conference on Computational Linguistics and Speech Processing (ROCLING 2023)

2022

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A Preliminary Study on Automated Speaking Assessment of English as a Second Language (ESL) Students
Tzu-I Wu | Tien-Hong Lo | Fu-An Chao | Yao-Ting Sung | Berlin Chen
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

Due to the surge in global demand for English as a second language (ESL), developments of automated methods for grading speaking proficiency have gained considerable attention. This paper aims to present a computerized regime of grading the spontaneous spoken language for ESL learners. Based on the speech corpus of ESL learners recently collected in Taiwan, we first extract multi-view features (e.g., pronunciation, fluency, and prosody features) from either automatic speech recognition (ASR) transcription or audio signals. These extracted features are, in turn, fed into a tree-based classifier to produce a new set of indicative features as the input of the automated assessment system, viz. the grader. Finally, we use different machine learning models to predict ESL learners’ respective speaking proficiency and map the result into the corresponding CEFR level. The experimental results and analysis conducted on the speech corpus of ESL learners in Taiwan show that our approach holds great potential for use in automated speaking assessment, meanwhile offering more reliable predictive results than the human experts.

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The Design and Development of a System for Chinese Character Difficulty and Features
Jung-En Haung | Hou-Chiang Tseng | Li-Yun Chang | Hsueh-Chih Chen | Yao-Ting Sung
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

Feature analysis of Chinese characters plays a prominent role in “character-based” education. However, there is an urgent need for a text analysis system for processing the difficulty of composing components for characters, primarily based on Chinese learners’ performance. To meet this need, the purpose of this research was to provide such a system by adapting a data-driven approach. Based on Chen et al.’s (2011) Chinese Orthography Database, this research has designed and developed an system: Character Difficulty - Research on Multi-features (CD-ROM). This system provides three functions: (1) analyzing a text and providing its difficulty regarding Chinese characters; (2) decomposing characters into components and calculating the frequency of components based on the analyzed text; and (3) affording component-deriving characters based on the analyzed text and downloadable images as teaching materials. With these functions highlighting multi-level features of characters, this system has the potential to benefit the fields of Chinese character instruction, Chinese orthographic learning, and Chinese natural language processing.

2021

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The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020
Fu-An Chao | Tien-Hong Lo | Shi-Yan Weng | Shih-Hsuan Chiu | Yao-Ting Sung | Berlin Chen
International Journal of Computational Linguistics & Chinese Language Processing, Volume 26, Number 1, June 2021

2019

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基於階層式編碼架構之文本可讀性預測(A Hierarchical Encoding Framework for Text Readability Prediction)
Shi-Yan Weng | Hou-Chiang Tseng | Yao-Ting Sung | Berlin Chen
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)

2018

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探索結合快速文本及卷積神經網路於可讀性模型之建立 (Exploring Combination of FastText and Convolutional Neural Networks for Building Readability Models) [In Chinese]
Hou-Chiang Tseng | Berlin Chen | Yao-Ting Sung
Proceedings of the 30th Conference on Computational Linguistics and Speech Processing (ROCLING 2018)

2017

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探究不同領域文件之可讀性分析 (Exploring Readability Analysis on Multi-Domain Texts) [In Chinese]
Hou-Chiang Tseng | Yao-Ting Sung | Berlin Chen
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)

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探究使用基於類神經網路之特徵於文本可讀性分類 (Exploring the Use of Neural Network based Features for Text Readability Classification) [In Chinese]
Hou-Chiang Tseng | Berlin Chen | Yao-Ting Sung
International Journal of Computational Linguistics & Chinese Language Processing, Volume 22, Number 2, December 2017-Special Issue on Selected Papers from ROCLING XXIX

2016

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基於深層類神經網路及表示學習技術之文件可讀性分類(Classification of Text Readability Based on Deep Neural Network and Representation Learning Techniques)[In Chinese]
Hou-Chiang Tseng | Hsiao-Tsung Hung | Yao-Ting Sung | Berlin Chen
Proceedings of the 28th Conference on Computational Linguistics and Speech Processing (ROCLING 2016)

2015

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融合多種深層類神經網路聲學模型與分類技術於華語錯誤發音檢測之研究(Exploring Combinations of Various Deep Neural Network based Acoustic Models and Classification Techniques for Mandarin Mispro-nunciation Detection)[In Chinese]
Yao-Chi Hsu | Ming-Han Yang | Hsiao-Tsung Hung | Yuwen Hsiung | Yao-Ting Hung | Berlin Chen
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)

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Automatically Detecting Syntactic Errors in Sentences Writing by Learners of Chinese as a Foreign Language
Tao-Hsing Chang | Yao-Ting Sung | Jia-Fei Hong
International Journal of Computational Linguistics & Chinese Language Processing, Volume 20, Number 1, June 2015-Special Issue on Chinese as a Foreign Language