International Journal of Computational Linguistics & Chinese Language Processing                                   [中æ�]
                                                                                          Vol. 26, No. 1, June 2021

Title:
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020

Author:
Fu-An Chao, Tien-Hong Lo, Shi-Yan Weng, Shih-Hsuan Chiu, Yao-Ting Sung, and Berlin Chen

Abstract:
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). FSR-2020 aims at fostering the development of Taiwanese speech recognition. Apart from the issues on tonal and dialectical variations of the Taiwanese language, speech artificially contaminated with different types of real-world noise also has to be dealt with in the final test stage; all of these make FSR-2020 much more challenging than before. To work around the under-resourced issue, the main technical aspects of our ASR system include various deep learning techniques, such as transfer learning, semi-supervised learning, front-end speech enhancement and model ensemble, as well as data cleansing and data augmentation conducted on the training data. With the best configuration, our system obtains 13.1 % syllable error rate (SER) on the final-test set, achieving the first place among all participating systems on Track 3.

Keywords:
Formosa Speech Recognition Challenge, Deep Learning, Transfer Learning, Semi-supervised Training

Title:
NSYSU-MITLab Speech Recognition System for Formosa Speech Recognition Challenge 2020

Author:
Hung-Pang Lin and Chia-Ping Chen

Abstract:
In this paper, we describe the system team NSYSU-MITLab implemented for Formosa Speech Recognition Challenge 2020. We use the Transformer architecture composed of Multi-head Attention to construct an end-to-end speech recognition system and combine it with Connectionist Temporal Classification (CTC) for end-to-end training and decoding. We have also built a deep neural network combined with a hidden Markov model (DNN-HMM). We use Time-Restricted Self-Attention and Factorized Time Delay Neural Network (TDNN-F) for the deep neural network in DNN-HMM. The best performance we have achieved with the proposed methods is the character error rate of 45.5% for Taiwan Southern Min Recommended Characters (�°æ�æ¼¢å�) task and syllable error rate 25.4% for Taiwan Minnanyu Luomazi Pinyin (�°ç��¼é𨺗) task.

Keywords:
Automatic Speech Recognition, Transformer, Conformer, Connectionist Temporal Classification, Acoustic Model

Title:
A Preliminary Study of Formosa Speech Recognition Challenge 2020 �� Taiwanese ASR

Author:
Fu-Hao Yu, Ke-Han Lu, Yi-Wei Wang,  Wei-Zhe Chang, Wei-Kai Huang and Kuan-Yu Chen

Abstract:
In order to study the effectiveness of the current deep learning-based speech recognition models in the speech recognition tasks of Taiwanese Southern Min Recommended Characters and Taiwan Minnanyu Luomazi Pinyin, this study uses the corpora provided by the 2020 Formosa Speech Recognition Challenge 2020 (FSR-2020) to evalutae some neural-based ASR systems by ESPnet and Kaldi toolkits. In the end, our system achieved a 66.1% error rate in the Taiwanese Southern Min Recommended Characters recognition (Track2), and the error rate we got in the Taiwan Minnanyu Luomazi Pinyin recognition (Track3) was 28.6%.

Keywords:
Taiwanese Southern Min Recommended Characters, Taiwan Minnanyu Luomazi Pinyin, Taiwanese ASR

Title:
Textual Relations with Conjunctive Adverbials in English Writing by Chinese Speakers:  A corpus-based Approach

Author:
Tung-Yu Kao, and Li-mei Chen

Abstract:
The study aims to investigate the use of conjunctive adverbials (CA, hereafter) performing various textual relations in the English writing by Chinese speakers across genres and over time. To begin with, a corpus of one million word was compiled and the corpus interface was constructed. Later, 45 pieces of writing by 5 college students during 4 semesters were selected for data annotation and analysis, with each student contributing 9 pieces for 9 text genres. The results show that there exists a distribution norm of CA-performed textual relations based on CA occurrence frequency and that the distribution is independent of genre and time effects. Compared with literature, the found distribution is also considered free from the first language influence. This suggests that the found distribution is a mental representation of mature human cognition, underlying English writing on global and coherent levels. Therefore, the found distribution is of great potential for developing automatic tools of discourse diagnosis.

Keywords:
Conjunctive Adverbial, Textual Relation, Text Genre, English Writing, Corpus Compilation, Automatic Discourse Diagnosis

Title:
The Analysis and Annotation of Propaganda Techniquesin Chinese News Texts

Author:
Meng-Hsien Shih, Ren-feng Duann, and Siaw-Fong Chung

Abstract:
In political news media, propaganda techniques are often employed to express one�䏭 political view, or to influence the audience�䏭 stance. Chinese corpora with the annotation of propaganda techniques are yet to be developed. In this paper, with an explainable approach, we annotated the use of propaganda techniques in Chinese political news texts, and enlarged the dataset by bootstrapping using a small set of manually annotated data. To ensure the validity, we manually corrected the bootstrapped dataset and ran a pilot machine-learning experiment using a naïve Bayes classifier trained with the bag-of-words feature. A precision of 74.26% was reached for the binary classification (with or without propaganda technique). The manually annotated data with propaganda techniques is available online for the application of machine training and learning to predict the stance of new texts.

Keywords:
Sentiment (Stance) Analysis, Language Resource, Propaganda Techniques, Taiwan News Media