ymoslem/acl-abstracts huggingface dataset:
Taiwanese-Accented Mandarin and English Multi-Speaker Talking-Face Synthesis System (2022)

This paper proposes a multi-speaker talking-face synthesis system. The system incorporates voice cloning and lip-syncing technology to achieve text-to-talking-face generation by acquiring audio and video clips of any speaker and using zero-shot transfer learning. In addition, we used open-source corpora to train several Taiwanese-accented models and proposed using Mandarin Phonetic Symbols (Bopomofo) as the character embedding of the synthesizer to improve the system`s ability to synthesize Chinese-English code-switched sentences. Through our system, users can create rich applications. Also, the research on this technology is novel in the audiovisual speech synthesis field.
Is Character Trigram Overlapping Ratio Still the Best Similarity Measure for Aligning Sentences in a Paraphrased Corpus? (2022)

Sentence alignment is an essential step in studying the mapping among different language expressions, and the character trigram overlapping ratio was reported to be the most effective similarity measure in aligning sentences in the text simplification dataset. However, the appropriateness of each similarity measure depends on the characteristics of the corpus to be aligned. This paper studies if the character trigram is still a suitable similarity measure for the task of aligning sentences in a paragraph paraphrasing corpus. We compare several embedding-based and non-embeddings model-agnostic similarity measures, including those that have not been studied previously. The evaluation is conducted on parallel paragraphs sampled from the Webis-CPC-11 corpus, which is a paragraph paraphrasing dataset. Our results show that modern BERT-based measures such as Sentence-BERT or BERTScore can lead to significant improvement in this task.
RoBERTa-based Traditional Chinese Medicine Named Entity Recognition Model (2022)

In this study, a named entity recognition was constructed and applied to the identification of Chinese medicine names and disease names. The results can be further used in a human-machine dialogue system to provide people with correct Chinese medicine medication reminders. First, this study uses web crawlers to sort out web resources into a Chinese medicine named entity corpus, collecting 1097 articles, 1412 disease names and 38714 Chinese medicine names. Then, we annotated each article using TCM name and BIO tagging method. Finally, this study trains and evaluates BERT, ALBERT, RoBERTa, GPT2 with BiLSTM and CRF. The experimental results show that RoBERTa`s NER system combining BiLSTM and CRF achieves the best system performance, with a precision rate of 0.96, a recall rate of 0.96, and an F1-score of 0.96.
A Study on Using Different Audio Lengths in Transfer Learning for Improving Chainsaw Sound Recognition (2022)

Chainsaw sound recognition is a challenging task because of the complexity of sound and the excessive noises in mountain environments. This study aims to discuss the influence of different sound lengths on the accuracy of model training. Therefore, this study used LeNet, a simple model with few parameters, and adopted the design of average pooling to enable the proposed models to receive audio of any length. In performance comparison, we mainly compared the influence of different audio lengths and further tested the transfer learning from short-to-long and long-to-short audio. In experiments, we used the ESC-10 dataset for training models and validated their performance via the self-collected chainsaw-audio dataset. The experimental results show that (a) the models trained with different audio lengths (1s, 3s, and 5s) have accuracy from 74{\%} 78{\%}, 74{\%} 77{\%}, and 79{\%} 83{\%} on the self-collected dataset. (b) The generalization of the previous models is significantly improved by transfer learning, the models achieved 85.28{\%}, 88.67{\%}, and 91.8{\%} of accuracy. (c) In transfer learning, the model learned from short-to-long audios can achieve better results than that learned from long-to-short audios, especially being differed 14{\%} of accuracy on 5s chainsaw-audios.
Using Grammatical and Semantic Correction Model to Improve Chinese-to-Taiwanese Machine Translation Fluency (2022)

Currently, there are three major issues to tackle in Chinese-to-Taiwanese machine translation: multi-pronunciation Taiwanese words, unknown words, and Chinese-to-Taiwanese grammatical and semantic transformation. Recent studies have mostly focused on the issues of multi-pronunciation Taiwanese words and unknown words, while very few research papers focus on grammatical and semantic transformation. However, there exist grammatical rules exclusive to Taiwanese that, if not translated properly, would cause the result to feel unnatural to native speakers and potentially twist the original meaning of the sentence, even with the right words and pronunciations. Therefore, this study collects and organizes a few common Taiwanese sentence structures and grammar rules, then creates a grammar and semantic correction model for Chinese-to-Taiwanese machine translation, which would detect and correct grammatical and semantic discrepancies between the two languages, thus improving translation fluency.
Investigation of feature processing modules and attention mechanisms in speaker verification system (2022)

In this paper, we use several combinations of feature front-end modules and attention mechanisms to improve the performance of our speaker verification system. An updated version of ECAPA-TDNN is chosen as a baseline. We replace and integrate different feature front-end and attention mechanism modules to compare and find the most effective model design, and this model would be our final system. We use VoxCeleb 2 dataset as our training set, and test the performance of our models on several test sets. With our final proposed model, we improved performance by 16{\%} over baseline on VoxSRC2022 valudation set, achieving better results for our speaker verification system.
A Preliminary Study of the Application of Discrete Wavelet Transform Features in Conv-TasNet Speech Enhancement Model (2022)

Nowadays, time-domain features have been widely used in speech enhancement (SE) networks like frequency-domain features to achieve excellent performance in eliminating noise from input utterances. This study primarily investigates how to extract information from time-domain utterances to create more effective features in speech enhancement. We present employing sub-signals dwelled in multiple acoustic frequency bands in time domain and integrating them into a unified feature set. We propose using the discrete wavelet transform (DWT) to decompose each input frame signal to obtain sub-band signals, and a projection fusion process is performed on these signals to create the ultimate features. The corresponding fusion strategy is the bi-projection fusion (BPF). In short, BPF exploits the sigmoid function to create ratio masks for two feature sources. The concatenation of fused DWT features and time features serves as the encoder output of a celebrated SE framework, fully-convolutional time-domain audio separation network (Conv-TasNet), to estimate the mask and then produce the enhanced time-domain utterances. The evaluation experiments are conducted on the VoiceBank-DEMAND and VoiceBank-QUT tasks. The experimental results reveal that the proposed method achieves higher speech quality and intelligibility than the original Conv-TasNet that uses time features only, indicating that the fusion of DWT features created from the input utterances can benefit time features to learn a superior Conv-TasNet in speech enhancement.
Exploiting the compressed spectral loss for the learning of the DEMUCS speech enhancement network (2022)

This study aims to improve a highly effective speech enhancement technique, DEMUCS, by revising the respective loss function in learning. DEMUCS, developed by Facebook Team, is built on the Wave-UNet and consists of convolutional layer encoding and decoding blocks with an LSTM layer in between. Although DEMUCS processes the input speech utterance purely in the time (wave) domain, the applied loss function consists of wave-domain L1 distance and multi-scale shorttime-Fourier-transform (STFT) loss. That is, both time- and frequency-domain features are taken into consideration in the learning of DEMUCS. In this study, we present revising the STFT loss in DEMUCS by employing the compressed magnitude spectrogram. The compression is done by either the power-law operation with a positive exponent less than one, or the logarithmic operation. We evaluate the presented novel framework on the VoiceBank-DEMAND database and task. The preliminary experimental results suggest that DEMUCS containing the power-law compressed magnitude spectral loss outperforms the original DEMUCS by providing the test utterances with higher objective quality and intelligibility scores (PESQ and STOI). Relatively, the logarithm compressed magnitude spectral loss does not benefit DEMUCS. Therefore, we reveal that DEMUCS can be further improved by properly revising the STFT terms of its loss function.
Using Machine Learning and Pattern-Based Methods for Identifying Elements in Chinese Judgment Documents of Civil Cases (2022)

Providing structural information about civil cases for judgement prediction systems or recommendation systems can enhance the efficiency of the inference procedures and the justifiability of produced results. In this research, we focus on the civil cases about alimony, which is a relatively uncommon choice in current applications of artificial intelligence in law. We attempt to identify the statements for four types of legal functions in judgement documents, i.e., the pleadings of the applicants, the responses of the opposite parties, the opinions of the courts, and uses of laws to reach the final decisions. In addition, we also try to identify the conflicting issues between the plaintiffs and the defendants in the judgement documents.
Development of Mandarin-English code-switching speech synthesis system (2022)

In this paper, the Mandarin-English code-switching speech synthesis system has been proposed. To focus on learning the content information between two languages, the training dataset is multilingual artificial dataset whose speaker style is unified. Adding language embedding into the system helps it be more adaptive to multilingual dataset. Besides, text preprocessing is applied and be used in different way which depends on the languages. Word segmentation and text-to-pinyin are the text preprocessing for Mandarin, which not only improves the fluency but also reduces the learning complexity. Number normalization decides whether the arabic numerals in sentence needs to add the digits. The preprocessing for English is acronym conversion which decides the pronunciation of acronym.
Predicting Judgments and Grants for Civil Cases of Alimony for the Elderly (2022)

The needs for mediation are increasing rapidly along with the increasing number of cases of the alimony for the elderly in recent years. Offering a prediction mechanism for predicting the outcomes of some prospective lawsuits may alleviate the workload of the mediation courts. This research aims to offer the predictions for the judgments and the granted alimony for the plaintiffs of such civil cases in Chinese, based on our analysis of results of the past lawsuits. We hope that the results can be helpful for both the involved parties and the courts. To build the current system, we segment and vectorize the texts of the judgement documents, and apply the logistic regression and model tree models for predicting the judgments and for estimating the granted alimony of the cases, respectively.
Lightweight Sound Event Detection Model with RepVGG Architecture (2022)

In this paper, we proposed RepVGGRNN, which is a light weight sound event detection model. We use RepVGG convolution blocks in the convolution part to improve performance, and re-parameterize the RepVGG blocks after the model is trained to reduce the parameters of the convolution layers. To further improve the accuracy of the model, we incorporated both the mean teacher method and knowledge distillation to train the lightweight model. The proposed system achieves PSDS (Polyphonic sound event detection score)-scenario 1, 2 of 40.8{\%} and 67.7{\%} outperforms the baseline system of 34.4{\%} and 57.2{\%} on the DCASE 2022 Task4 validation dataset. The quantity of the parameters in the proposed system is about 49.6K, only 44.6{\%} of the baseline system.
Analyzing discourse functions with acoustic features and phone embeddings: non-lexical items in Taiwan Mandarin (2022)

Non-lexical items are expressive devices used in conversations that are not words but are nevertheless meaningful. These items play crucial roles, such as signaling turn-taking or marking stances in interactions. However, as the non-lexical items do not stably correspond to written or phonological forms, past studies tend to focus on studying their acoustic properties, such as pitches and durations. In this paper, we investigate the discourse functions of non-lexical items through their acoustic properties and the phone embeddings extracted from a deep learning model. Firstly, we create a non-lexical item dataset based on the interpellation video clips from Taiwan`s Legislative Yuan. Then, we manually identify the non-lexical items and their discourse functions in the videos. Next, we analyze the acoustic properties of those items through statistical modeling and building classifiers based on phone embeddings extracted from a phone recognition model. We show that (1) the discourse functions have significant effects on the acoustic features; and (2) the classifiers built on phone embeddings perform better than the ones on conventional acoustic properties. These results suggest that phone embeddings may reflect the phonetic variations crucial in differentiating the discourse functions of non-lexical items.
A Dimensional Valence-Arousal-Irony Dataset for Chinese Sentence and Context (2022)

Chinese multi-dimensional sentiment detection is a challenging task with a considerable impact on semantic understanding. Past irony datasets are utilized to annotate sentiment type of whole sentences of irony. It does not provide the corresponding intensity of valence and arousal on the sentences and context. However, an ironic statement is defined as a statement whose apparent meaning is the opposite of its actual meaning. This means that in order to understand the actual meaning of a sentence, contextual information is needed. Therefore, the dimensional sentiment intensities of ironic sentences and context are important issues in the natural language processing field. This paper creates the extended NTU irony corpus, which includes valence, arousal and irony intensities on sentence-level; and valence and arousal intensities on context-level, called Chinese Dimensional Valence-Arousal-Irony (CDVAI) dataset. Therefore, this paper analyzes the annotation difference between the human annotators and uses a deep learning model such as BERT to evaluate the prediction performances on CDVAI corpus.
Intelligent Future Recreation Harbor Application Service: Taking Kaohsiung Asia New Bay as an Example to Construct a Composite Recreational Knowledge Graph (2022)

In view of the lack of overall specialized design services for harbour recreation in Taiwan nowadays, various marine recreational activities and marine scenic spots haven`t yet been planned and developed in the integration of services around the city and harbour. As there are not many state-of-the-art products and application services, and Taiwan`s harbour leisure services-related industries are facing the challenge of digital transformation. Institute for Information Industry proposed an innovative {\textquotedblleft}Smart Future Recreational Harbour Application Service{\textquotedblright} project, taking Kaohsiung Asia`s New Bay Area as the main field of demonstration, Using multi-source knowledge graph integration and inference technology to recommend appropriate recreational service information, as a result, tourists can enjoy the best virtual reality intelligent human-machine interactive service experience during their trip.
HanTrans: An Empirical Study on Cross-Era Transferability of Chinese Pre-trained Language Model (2022)

The pre-trained language model has recently dominated most downstream tasks in the NLP area. Particularly, bidirectional Encoder Representations from Transformers (BERT) is the most iconic pre-trained language model among the NLP tasks. Their proposed masked-language modeling (MLM) is an indispensable part of the existing pre-trained language models. Those outperformed models for downstream tasks benefited directly from the large training corpus in the pre-training stage. However, their training corpus for modern traditional Chinese was light. Most of all, the ancient Chinese corpus is still disappearance in the pre-training stage. Therefore, we aim to address this problem by transforming the annotation data of ancient Chinese into BERT style training corpus. Then we propose a pre-trained Oldhan Chinese BERT model for the NLP community. Our proposed model outperforms the original BERT model by significantly reducing perplexity scores in masked-language modeling (MLM). Also, our fine-tuning models improve F1 scores on word segmentation and part-of-speech tasks. Then we comprehensively study zero-shot cross-eras ability in the BERT model. Finally, we visualize and investigate personal pronouns in the embedding space of ancient Chinese records from four eras. We have released our code at \url{https://github.com/ckiplab/han-transformers}.
A Preliminary Study on Automated Speaking Assessment of English as a Second Language (ESL) Students (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.
Clustering Issues in Civil Judgments for Recommending Similar Cases (2022)

Similar judgments search is an important task in legal practice, from which valuable legal insights can be obtained. Issues are disputes between both parties in civil litigation, which represents the core topics to be considered in the trials. Many studies calculate the similarity between judgments from different perspectives and methods. We first cluster the issues in the judgments, and then encode the judgments with vectors for whether or not the judgments contain issues in the corresponding clusters. The similarity between the judgments are evaluated based on the encoded messages. We verify the effectiveness of the system with a human scoring process by a legal background assistant, while comparing the effects of several combinations of preprocessing steps and selections of clustering strategies.
Multifaceted Assessments of Traditional Chinese Word Segmentation Tool on Large Corpora (2022)

This study aims to evaluate three most popular word segmentation tool for a large Traditional Chinese corpus in terms of their efficiency, resource consumption, and cost. Specifically, we compare the performances of Jieba, CKIP, and MONPA on word segmentation, part-of-speech tagging and named entity recognition through extensive experiments. Experimental results show that MONPA using GPU for batch segmentation can greatly reduce the processing time of massive datasets. In addition, its features such as word segmentation, part-of-speech tagging, and named entity recognition are beneficial to downstream applications.
Mandarin-English Code-Switching Speech Recognition System for Specific Domain (2022)

This paper will introduce the use of Automatic Speech Recognition (ASR) technology to process speech content with specific domain. We will use the Conformer end-to-end model as the system architecture, and use pure Chinese data for initial training. Next, use the transfer learning technology to fine-tune the system with Mandarin-English code-switching data. Finally, use the Mandarin-English code-switching data with a specific domain makes the final fine-tuning of the model so that it can achieve a certain effect on speech recognition in a specific domain. Experiments with different fine-tuning methods reduce the final error rate from 82.0{\%} to 34.8{\%}.
Legal Case Winning Party Prediction With Domain Specific Auxiliary Models (2022)

Sifting through hundreds of old case documents to obtain information pertinent to the case in hand has been a major part of the legal profession for centuries. However, with the expansion of court systems and the compounding nature of case law, this task has become more and more intractable with time and resource constraints. Thus automation by Natural Language Processing presents itself as a viable solution. In this paper, we discuss a novel approach for predicting the winning party of a current court case by training an analytical model on a corpus of prior court cases which is then run on the prepared text on the current court case. This will allow legal professionals to efficiently and precisely prepare their cases to maximize the chance of victory. The model is built with and experimented using legal domain specific sub-models to provide more visibility to the final model, along with other variations. We show that our model with critical sentence annotation with a transformer encoder using RoBERTa based sentence embedding is able to obtain an accuracy of 75.75{\%}, outperforming other models.
Early Speech Production in Infants and Toddlers Later Diagnosed with Cerebral Palsy: A Retrospective Study (2022)

In this retrospective study, we compared the early speech development between infants with cerebral palsy (CP) and typically developing (TD) infants. The recordings of utterances were collected from two CP infants and two typically-developing (TD) infants at approximately 8 and 24 months old. The data was analyzed by volubility, consonant emergence, canonical babbling ratio (CBR), mean babbling level (MBL). The major findings show that comparing with TD group, CP group has the characteristics of: 1) lower volubility 2) CBRutter below 0.15 at 2 years old 3) MBL score below 2 at the age of 2 with a feature of above 95{\%} in level 1 4) using consonants mainly at two oral places (bilabials and velars) and three manners of articulation (nasal, fricative, and stop) at 2 years old.
Automatic Generation of Abstracts for Research Papers (2022)

Summarizing has always been an important utility for reading long documents. Research papers are unique in this regard, as they have a compulsory summary in the form of the abstract in the beginning of the document which gives the gist of the entire study often within a set upper limit for the word count. Writing the abstract to be sufficiently succinct while being descriptive enough is a hard task even for native English speakers. This study is the first step in generating abstracts for research papers in the computational linguistics domain automatically using the domain-specific abstractive summarization power of the GPT-Neo model.