ymoslem/acl-abstracts huggingface dataset:
Construction of Responsive Utterance Corpus for Attentive Listening Response Production (2022)

In Japan, the number of single-person households, particularly among the elderly, is increasing. Consequently, opportunities for people to narrate are being reduced. To address this issue, conversational agents, e.g., communication robots and smart speakers, are expected to play the role of the listener. To realize these agents, this paper describes the collection of conversational responses by listeners that demonstrate attentive listening attitudes toward narrative speakers, and a method to annotate existing narrative speech with responsive utterances is proposed. To summarize, 148,962 responsive utterances by 11 listeners were collected in a narrative corpus comprising 13,234 utterance units. The collected responsive utterances were analyzed in terms of response frequency, diversity, coverage, and naturalness. These results demonstrated that diverse and natural responsive utterances were collected by the proposed method in an efficient and comprehensive manner. To demonstrate the practical use of the collected responsive utterances, an experiment was conducted, in which response generation timings were detected in narratives.
Speak: A Toolkit Using Amazon Mechanical Turk to Collect and Validate Speech Audio Recordings (2022)

We present Speak, a toolkit that allows researchers to crowdsource speech audio recordings using Amazon Mechanical Turk (MTurk). Speak allows MTurk workers to submit speech recordings in response to a task prompt and stimulus (e.g. image, text excerpt, audio file) defined by researchers, a functionality that is not natively offered by MTurk at the time of writing this paper. Importantly, the toolkit employs numerous measures to ensure that speech recordings collected are of adequate quality, in order to avoid accepting unusable data and prevent abuse/fraud. Speak has demonstrated utility, having collected over 600,000 recordings to date. The toolkit is open-source and available for download.
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation (2022)

Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND`s design and procedure for collecting the speech data, including annotations. ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. Furthermore, we conduct baseline experiments using pre-trained wav2vec 2.0 models, achieving a best performance of 22.69{\%} character error rate and 27.05{\%} mixed error rate.
A Romanization System and WebMAUS Aligner for Arabic Varieties (2022)

This paper presents the results of an ongoing collaboration to develop an Arabic variety-independent romanization system that aims to homogenize and simplify the romanization of the Arabic script, and introduces an Arabic variety-independent WebMAUS service offering a free to use forced-alignment service fully integrated within the WebMAUS services. We present the rationale for developing such a system, highlighting the need for a detailed romanization system with graphemes corresponding to the phonemic short and long vowels/consonants in Arabic varieties. We describe how the acoustic model was created, followed by several hands-on recipes for applying the forced alignment webservice either online or programatically. Finally, we discuss some of the issues we faced during the development of the system.
BembaSpeech: A Speech Recognition Corpus for the Bemba Language (2022)

We present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting over 24 hours of read speech in the Bemba language, a written but low-resourced language spoken by over 30{\%} of the population in Zambia. To assess its usefulness for training and testing ASR systems for Bemba, we explored different approaches; supervised pre-training (training from scratch), cross-lingual transfer learning from a monolingual English pre-trained model using DeepSpeech on the portion of the dataset and fine-tuning large scale self-supervised Wav2Vec2.0 based multilingual pre-trained models on the complete BembaSpeech corpus. From our experiments, the 1 billion XLS-R parameter model gives the best results. The model achieves a word error rate (WER) of 32.91{\%}, results demonstrating that model capacity significantly improves performance and that multilingual pre-trained models transfers cross-lingual acoustic representation better than monolingual pre-trained English model on the BembaSpeech for the Bemba ASR. Lastly, results also show that the corpus can be used for building ASR systems for Bemba language.
BehanceCC: A ChitChat Detection Dataset For Livestreaming Video Transcripts (2022)

Livestreaming videos have become an effective broadcasting method for both video sharing and educational purposes. However, livestreaming videos contain a considerable amount of off-topic content (i.e., up to 50{\%}) which introduces significant noises and data load to downstream applications. This paper presents BehanceCC, a new human-annotated benchmark dataset for off-topic detection (also called chitchat detection) in livestreaming video transcripts. In addition to describing the challenges of the dataset, our extensive experiments of various baselines reveal the complexity of chitchat detection for livestreaming videos and suggest potential future research directions for this task. The dataset will be made publicly available to foster research in this area.
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection (2022)

With the advent of the General Data Protection Regulation (GDPR) and increasing privacy concerns, the sharing of speech data is faced with significant challenges. Protecting the sensitive content of speech is the same important as the voiceprint. This paper proposes an effective speech content protection method by constructing a frame-by-frame adversarial speech generation system. We revisited the adversarial examples generating method in the recent machine learning field and selected the phonetic state sequence of sensitive speech for the adversarial examples generation. We build an adversarial speech collection. Moreover, based on the speech collection, we proposed a neural network-based frame-by-frame mapping method to recover the speech content by converting from the adversarial speech to the human speech. Experiment shows our proposed method can encode and recover any sensitive audio, and our method is easy to be conducted with publicly available resources of speech recognition technology.
A new European Portuguese corpus for the study of Psychosis through speech analysis (2022)

Psychosis is a clinical syndrome characterized by the presence of symptoms such as hallucinations, thought disorder and disorganized speech. Several studies have used machine learning, combined with speech and natural language processing methods to aid in the diagnosis process of this disease. This paper describes the creation of the first European Portuguese corpus for the identification of the presence of speech characteristics of psychosis, which contains samples of 92 participants, 56 controls and 36 individuals diagnosed with psychosis and medicated. The corpus was used in a set of experiments that allowed identifying the most promising feature set to perform the classification: the combination of acoustic and speech metric features. Several classifiers were implemented to study which ones entailed the best performance depending on the task and feature set. The most promising results obtained for the entire corpus were achieved when identifying individuals with a Multi-Layer Perceptron classifier and reached an 87.5{\%} accuracy. Focusing on the gender dependent results, the overall best results were 90.9{\%} and 82.9{\%} accuracy, for female and male subjects respectively. Lastly, the experiments performed lead us to conjecture that spontaneous speech presents more identifiable characteristics than read speech to differentiate healthy and patients diagnosed with psychosis.
Investigating Inter- and Intra-speaker Voice Conversion using Audiobooks (2022)

Audiobook readers play with their voices to emphasize some text passages, highlight discourse changes or significant events, or in order to make listening easier and entertaining. A dialog is a central passage in audiobooks where the reader applies significant voice transformation, mainly prosodic modifications, to realize character properties and changes. However, these intra-speaker modifications are hard to reproduce with simple text-to-speech synthesis. The manner of vocalizing characters involved in a given story depends on the text style and differs from one speaker to another. In this work, this problem is investigated through the prism of voice conversion. We propose to explore modifying the narrator`s voice to fit the context of the story, such as the character who is speaking, using voice conversion. To this end, two complementary experiments are designed: the first one aims to assess the quality of our Phonetic PosteriorGrams (PPG)-based voice conversion system using parallel data. Subjective evaluations with naive raters are conducted to estimate the quality of the signal generated and the speaker similarity. The second experiment applies an intra-speaker voice conversion, considering narration passages and direct speech passages as two distinct speakers. Data are then nonparallel and the dissimilarity between character and narrator is subjectively measured.
Multilingual Transfer Learning for Children Automatic Speech Recognition (2022)

Despite recent advances in automatic speech recognition (ASR), the recognition of children`s speech still remains a significant challenge. This is mainly due to the high acoustic variability and the limited amount of available training data. The latter problem is particularly evident in languages other than English, which are usually less-resourced. In the current paper, we address children ASR in a number of less-resourced languages by combining several small-sized children speech corpora from these languages. In particular, we address the following research question: Does a novel two-step training strategy in which multilingual learning is followed by language-specific transfer learning outperform conventional single language/task training for children speech, as well as multilingual and transfer learning alone? Based on previous experimental results with English, we hypothesize that multilingual learning provides a better generalization of the underlying characteristics of children`s speech. Our results provide a positive answer to our research question, by showing that using transfer learning on top of a multilingual model for an unseen language outperforms conventional single language-specific learning.
BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts (2022)

Question-Answer (QA) is one of the effective methods for storing knowledge which can be used for future retrieval. As such, identifying mentions of questions and their answers in text is necessary for a knowledge construction and retrieval systems. In the literature, QA identification has been well studied in the NLP community. However, most of the prior works are restricted to formal written documents such as papers or websites. As such, Questions and Answers that are presented in informal/noisy documents have not been adequately studied. One of the domains that can significantly benefit from QA identification is the domain of livestreaming video transcripts that involve abundant QA pairs to provide valuable knowledge for future users and services. Since video transcripts are often transcribed automatically for scale, they are prone to errors. Combined with the informal nature of discussion in a video, prior QA identification systems might not be able to perform well in this domain. To enable comprehensive research in this domain, we present a large-scale QA identification dataset annotated by human over transcripts of 500 hours of streamed videos. We employ Behance.net to collect the videos and their automatically obtained transcripts. Furthermore, we conduct extensive analysis on the annotated dataset to understand the complexity of QA identification for livestreaming video transcripts. Our experiments show that the annotated dataset presents unique challenges for existing methods and more research is necessary to explore more effective methods. The dataset and the models developed in this work will be publicly released for future research.
Bidirectional Skeleton-Based Isolated Sign Recognition using Graph Convolutional Networks (2022)

To improve computer-based recognition from video of isolated signs from American Sign Language (ASL), we propose a new skeleton-based method that involves explicit detection of the start and end frames of signs, trained on the ASLLVD dataset; it uses linguistically relevant parameters based on the skeleton input. Our method employs a bidirectional learning approach within a Graph Convolutional Network (GCN) framework. We apply this method to the WLASL dataset, but with corrections to the gloss labeling to ensure consistency in the labels assigned to different signs; it is important to have a 1-1 correspondence between signs and text-based gloss labels. We achieve a success rate of 77.43{\%} for top-1 and 94.54{\%} for top-5 using this modified WLASL dataset. Our method, which does not require multi-modal data input, outperforms other state-of-the-art approaches on the same modified WLASL dataset, demonstrating the importance of both attention to the start and end frames of signs and the use of bidirectional data streams in the GCNs for isolated sign recognition.
Deep learning-based end-to-end spoken language identification system for domain-mismatched scenario (2022)

Domain mismatch is a critical issue when it comes to spoken language identification. To overcome the domain mismatch problem, we have applied several architectures and deep learning strategies which have shown good results in cross-domain speaker verification tasks to spoken language identification. Our systems were evaluated on the Oriental Language Recognition (OLR) Challenge 2021 Task 1 dataset, which provides a set of cross-domain language identification trials. Among our experimented systems, the best performance was achieved by using the mel frequency cepstral coefficient (MFCC) and pitch features as input and training the ECAPA-TDNN system with a flow-based regularization technique, which resulted in a Cavg of 0.0631 on the OLR 2021 progress set.
Handwritten Character Generation using Y-Autoencoder for Character Recognition Model Training (2022)

It is well-known that the deep learning-based optical character recognition (OCR) system needs a large amount of data to train a high-performance character recognizer. However, it is costly to collect a large amount of realistic handwritten characters. This paper introduces a Y-Autoencoder (Y-AE)-based handwritten character generator to generate multiple Japanese Hiragana characters with a single image to increase the amount of data for training a handwritten character recognizer. The adaptive instance normalization (AdaIN) layer allows the generator to be trained and generate handwritten character images without paired-character image labels. The experiment shows that the Y-AE could generate Japanese character images then used to train the handwritten character recognizer, producing an F1-score improved from 0.8664 to 0.9281. We further analyzed the usefulness of the Y-AE-based generator with shape images, out-of-character (OOC) images, which have different character images styles in model training. The result showed that the generator could generate a handwritten image with a similar style to that of the input character.
Attention-Focused Adversarial Training for Robust Temporal Reasoning (2022)

We propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the adversarial perturbation only to the embedding layer, ignoring the other layers of the model, which might limit the generalization power of adversarial training. Instead, our algorithm searches for the best combination of layers to add the adversarial perturbation. We add the adversarial perturbation to multiple hidden states or attention representations of the model layers. Adding the perturbation to the attention representations performed best in our experiments. Our model can improve performance on several temporal reasoning benchmarks, and establishes new state-of-the-art results.
PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter (2022)

Transformer-based models have become the state-of-the-art for numerous natural language processing (NLP) tasks, especially for noisy data sets, including social media posts. For example, BERTweet, pre-trained RoBERTa on a large amount of Twitter data, has achieved state-of-the-art results on several Twitter NLP tasks. We argue that it is not only important to have general pre-trained models for a social media platform, but also domain-specific ones that better capture domain-specific language context. Domain-specific resources are not only important for NLP tasks associated with a specific domain, but they are also useful for understanding language differences across domains. One domain that receives a large amount of attention is politics, more specifically political elections. Towards that end, we release PoliBERTweet, a pre-trained language model trained from BERTweet on over 83M US 2020 election-related English tweets. While the construction of the resource is fairly straightforward, we believe that it can be used for many important downstream tasks involving language, including political misinformation analysis and election public opinion analysis. To show the value of this resource, we evaluate PoliBERTweet on different NLP tasks. The results show that our model outperforms general-purpose language models in domain-specific contexts, highlighting the value of domain-specific models for more detailed linguistic analysis. We also extend other existing language models with a sample of these data and show their value for presidential candidate stance detection, a context-specific task. We release PoliBERTweet and these other models to the community to advance interdisciplinary research related to Election 2020.
Modeling the Impact of Syntactic Distance and Surprisal on Cross-Slavic Text Comprehension (2022)

We focus on the syntactic variation and measure syntactic distances between nine Slavic languages (Belarusian, Bulgarian, Croatian, Czech, Polish, Slovak, Slovene, Russian, and Ukrainian) using symmetric measures of insertion, deletion and movement of syntactic units in the parallel sentences of the fable {\textquotedblleft}The North Wind and the Sun{\textquotedblright}. Additionally, we investigate phonetic and orthographic asymmetries between selected languages by means of the information theoretical notion of surprisal. Syntactic distance and surprisal are, thus, considered as potential predictors of mutual intelligibility between related languages. In spoken and written cloze test experiments for Slavic native speakers, the presented predictors will be validated as to whether variations in syntax lead to a slower or impeded intercomprehension of Slavic texts.
BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification (2022)

This research provides the first comprehensive analysis of the performance of pre-trained language models for Sinhala text classification. We test on a set of different Sinhala text classification tasks and our analysis shows that out of the pre-trained multilingual models that include Sinhala (XLM-R, LaBSE, and LASER), XLM-R is the best model by far for Sinhala text classification. We also pre-train two RoBERTa-based monolingual Sinhala models, which are far superior to the existing pre-trained language models for Sinhala. We show that when fine-tuned, these pre-trained language models set a very strong baseline for Sinhala text classification and are robust in situations where labeled data is insufficient for fine-tuning. We further provide a set of recommendations for using pre-trained models for Sinhala text classification. We also introduce new annotated datasets useful for future research in Sinhala text classification and publicly release our pre-trained models.
Pre-training and Evaluating Transformer-based Language Models for Icelandic (2022)

In this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types of monolingual ELECTRA and ConvBERT models and compare our results to a previously trained monolingual RoBERTa model and the multilingual mBERT model. We find that the Transformer models obtain better results, often by a large margin, compared to previous state-of-the-art models. Furthermore, our results indicate that pre-training larger language models results in a significant reduction in error rates in comparison to smaller models. Finally, our results show that the monolingual models for Icelandic outperform a comparably sized multilingual model.
Exploring the Influence of Dialog Input Format for Unsupervised Clinical Questionnaire Filling (2022)

In the medical field, we have seen the emergence of health-bots that interact with patients to gather data and track their state. One of the downstream application is automatic questionnaire filling, where the content of the dialog is used to automatically fill a pre-defined medical questionnaire. Previous work has shown that answering questions from the dialog context can successfully be cast as a Natural Language Inference (NLI) task and therefore benefit from current pre-trained NLI models. However, NLI models have mostly been trained on text rather than dialogs, which may have an influence on their performance. In this paper, we study the influence of content transformation and content selection on the questionnaire filling task. Our results demonstrate that dialog pre-processing can significantly improve the performance of zero-shot questionnaire filling models which take health-bots dialogs as input.