Search Results for author: Tien-Hong Lo

Found 23 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.

Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition

no code implementations13 Jun 2021 Shih-Hsuan Chiu, Tien-Hong Lo, Fu-An Chao, Berlin Chen

In view of this, we in this paper seek to represent the historical context information of an utterance as graph-structured data so as to distill cross-utterances, global word interaction relationships.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

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

Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection

no code implementations27 Oct 2020 Wen-Ting Tseng, Tien-Hong Lo, Yung-Chang Hsu, Berlin Chen

To this end, predominant approaches to FAQ retrieval typically rank question-answer pairs by considering either the similarity between the query and a question (q-Q), the relevance between the query and the associated answer of a question (q-A), or combining the clues gathered from the q-Q similarity measure and the q-A relevance measure.

Language Modelling Retrieval +1

An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features

no code implementations1 Jun 2020 Shi-Yan Weng, Tien-Hong Lo, Berlin Chen

Tremendous amounts of multimedia associated with speech information are driving an urgent need to develop efficient and effective automatic summarization methods.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

An Effective End-to-End Modeling Approach for Mispronunciation Detection

no code implementations18 May 2020 Tien-Hong Lo, Shi-Yan Weng, Hsiu-jui Chang, Berlin Chen

Recently, end-to-end (E2E) automatic speech recognition (ASR) systems have garnered tremendous attention because of their great success and unified modeling paradigms in comparison to conventional hybrid DNN-HMM ASR systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge

no code implementations18 May 2020 Tien-Hong Lo, Fu-An Chao, Shi-Yan Weng, Berlin Chen

This paper describes the NTNU ASR system participating in the Interspeech 2020 Non-Native Children's Speech ASR Challenge supported by the SIG-CHILD group of ISCA.

Data Augmentation Language Modelling

Cannot find the paper you are looking for? You can Submit a new open access paper.