Search Results for author: Yao-Ting Sung

Found 12 papers, 0 papers with code

A Preliminary Study on Automated Speaking Assessment of English as a Second Language (ESL) Students

no code implementations ROCLING 2022 Tzu-I Wu, Tien-Hong Lo, Fu-An Chao, Yao-Ting Sung, Berlin Chen

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.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution

no code implementations11 Apr 2024 Tien-Hong Lo, Fu-An Chao, Tzu-I Wu, Yao-Ting Sung, Berlin Chen

Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner's speech.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multi-granular Pronunciation Assessment

no code implementations29 May 2023 Fu-An Chao, Tien-Hong Lo, Tzu-I Wu, Yao-Ting Sung, Berlin Chen

Automatic Pronunciation Assessment (APA) plays a vital role in Computer-assisted Pronunciation Training (CAPT) when evaluating a second language (L2) learner's speaking proficiency.

Automatic Speech Recognition Multi-Task Learning +4

Improving End-To-End Modeling for Mispronunciation Detection with Effective Augmentation Mechanisms

no code implementations17 Oct 2021 Tien-Hong Lo, Yao-Ting Sung, Berlin Chen

Recently, end-to-end (E2E) models, which allow to take spectral vector sequences of L2 (second-language) learners' utterances as input and produce the corresponding phone-level sequences as output, have attracted much research attention in developing mispronunciation detection (MD) systems.

The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020

no code implementations IJCLCLP 2021 Fu-An Chao, Tien-Hong Lo, Shi-Yan Weng, Shih-Hsuan Chiu, Yao-Ting Sung, Berlin Chen

This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW).

Data Augmentation Speech Enhancement +3

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