pwc-archive/papers-with-abstracts huggingface dataset:
An Annotation Framework for Luxembourgish Sentiment Analysis
Authors: Joshgun Sirajzade, Daniela Gierschek, Christoph Schommer
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, POS, Sentence, Sentiment Analysis, Sentiment Classification

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and facilitates sentence-level annotation of several annotators in parallel. In the heart of our framework is an XML database, which serves as central part linking several components. The corpus in the database consists of news articles and user comments. One of the components is LuNa, a tool for linguistic preprocessing of the data set. It tokenizes the text, splits it into sentences and assigns POS-tags to the tokens. After that, the preprocessed text is stored in XML format into the database. The Sentiment Annotation Tool, which is a browser-based tool, then enables the annotation of split sentences from the database. The Sentiment Engine, a separate module, is trained with this material in order to annotate the whole data set and analyze the sentiment of the comments over time and in relationship to the news articles. The gained knowledge can again be used to improve the sentiment classification on the one hand and on the other hand to understand the sentiment phenomenon from the linguistic point of view.
Speech-Emotion Detection in an Indonesian Movie
Authors: Fahmi Fahmi, Meganingrum Arista Jiwanggi, Mirna Adriani
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Emotion Recognition, General Classification

The growing demand to develop an automatic emotion recognition system for the Human-Computer Interaction field had pushed some research in speech emotion detection. Although it is growing, there is still little research about automatic speech emotion detection in Bahasa Indonesia. Another issue is the lack of standard corpus for this research area in Bahasa Indonesia. This study proposed several approaches to detect speech-emotion in the dialogs of an Indonesian movie by classifying them into 4 different emotion classes i.e. happiness, sadness, anger, and neutral. There are two different speech data representations used in this study i.e. statistical and temporal/sequence representations. This study used Artificial Neural Network (ANN), Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) variation, word embedding, and also the hybrid of three to perform the classification task. The best accuracies given by one-vs-rest scenario for each emotion class with speech-transcript pairs using hybrid of non-temporal and embedding approach are 1) happiness: 76.31{\%}; 2) sadness: 86.46{\%}; 3) anger: 82.14{\%}; and 4) neutral: 68.51{\%}. The multiclass classification resulted in 64.66{\%} of precision, 66.79{\%} of recall, and 64.83{\%} of F1-score.
Macsen: A Voice Assistant for Speakers of a Lesser Resourced Language
Authors: Dewi Jones
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, speech-recognition, Speech Recognition, Transfer Learning

This paper reports on the development of a voice assistant mobile app for speakers of a lesser resourced language {--} Welsh. An assistant with a smaller set of effective but useful skills is both desirable and urgent for the wider Welsh speaking community. Descriptions of the app{'}s skills, architecture, design decisions and user interface is provided before elaborating on the most recent research and activities in open source speech technology for Welsh. The paper reports on the progress to date on crowdsourcing Welsh speech data in Mozilla Common Voice and of its suitability for training Mozilla{'}s DeepSpeech speech recognition for a voice assistant application according to conventional and transfer learning methods. We demonstrate that with smaller datasets of speech data, transfer learning and a domain specific language model, acceptable speech recognition is achievable that facilitates, as confirmed by beta users, a practical and useful voice assistant for Welsh speakers. We hope that this work informs and serves as a model to researchers and developers in other lesser-resourced linguistic communities and helps bring into being voice assistant apps for their languages.
Gender Detection from Human Voice Using Tensor Analysis
Authors: Prasanta Roy, Parabattina Bhagath, Pradip Das
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: speech-recognition, Speech Recognition

Speech-based communication is one of the most preferred modes of communication for humans. The human voice contains several important information and clues that help in interpreting the voice message. The gender of the speaker can be accurately guessed by a person based on the received voice of a speaker. The knowledge of the speaker{'}s gender can be a great aid to design accurate speech recognition systems. GMM based classifier is a popular choice used for gender detection. In this paper, we propose a Tensor-based approach for detecting the gender of a speaker and discuss its implementation details for low resourceful languages. Experiments were conducted using the TIMIT and SHRUTI dataset. An average gender detection accuracy of 91{\%} is recorded. Analysis of the results with the proposed method is presented in this paper.
Data-Driven Parametric Text Normalization: Rapidly Scaling Finite-State Transduction Verbalizers to New Languages
Authors: S Ritchie, y, Eoin Mahon, Kim Heiligenstein, Nikos Bampounis, Daan van Esch, Christian Schallhart, Jonas Mortensen, Benoit Brard
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Text Normalization

This paper presents a methodology for rapidly generating FST-based verbalizers for ASR and TTS systems by efficiently sourcing language-specific data. We describe a questionnaire which collects the necessary data to bootstrap the number grammar induction system and parameterize the verbalizer templates described in Ritchie et al. (2019), and a machine-readable data store which allows the data collected through the questionnaire to be supplemented by additional data from other sources. This system allows us to rapidly scale technologies such as ASR and TTS to more languages, including low-resource languages.
Lenition and Fortition of Stop Codas in Romanian
Authors: Mathilde Hutin, Oana Niculescu, Ioana Vasilescu, Lori Lamel, Martine Adda-Decker
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The present paper aims at providing a first study of lenition- and fortition-type phenomena in coda position in Romanian, a language that can be considered as less-resourced. Our data show that there are two contexts for devoicing in Romanian: before a voiceless obstruent, which means that there is regressive voicelessness assimilation in the language, and before pause, which means that there is a tendency towards final devoicing proper. The data also show that non-canonical voicing is an instance of voicing assimilation, as it is observed mainly before voiced consonants (voiced obstruents and sonorants alike). Two conclusions can be drawn from our analyses. First, from a phonetic point of view, the two devoicing phenomena exhibit the same behavior regarding place of articulation of the coda, while voicing assimilation displays the reverse tendency. In particular, alveolars, which tend to devoice the most, also voice the least. Second, the two assimilation processes have similarities that could distinguish them from final devoicing as such. Final devoicing seems to be sensitive to speech style and gender of the speaker, while assimilation processes do not. This may indicate that the two kinds of processes are phonologized at two different degrees in the language, assimilation being more accepted and generalized than final devoicing.
Adapting a Welsh Terminology Tool to Develop a Cornish Dictionary
Authors: Delyth Prys
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Cornish and Welsh are closely related Celtic languages and this paper provides a brief description of a recent project to publish an online bilingual English/Cornish dictionary, the Gerlyver Kernewek, based on similar work previously undertaken for Welsh. Both languages are endangered, Cornish critically so, but both can benefit from the use of language technology. Welsh has previous experience of using language technologies for language revitalization, and this is now being used to help the Cornish language create new tools and resources, including lexicographical ones, helping a dispersed team of language specialists and editors, many of them in a voluntary capacity, to work collaboratively online. Details are given of the Maes T dictionary writing and publication platform, originally developed for Welsh, and of some of the adaptations that had to be made to accommodate the specific needs of Cornish, including their use of Middle and Late varieties due to its development as a revived language.
Automatic Extraction of Verb Paradigms in Regional Languages: the case of the Linguistic Crescent varieties
Authors: elena knyazeva, Gilles Adda, Philippe Boula de Mare{\"u}il, Maximilien Gu{\'e}rin, Nicolas Quint
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Language documentation is crucial for endangered varieties all over the world. Verb conjugation is a key aspect of this documentation for Romance varieties such as those spoken in central France, in the area of the Linguistic Crescent, which extends overs significant portions of the old provinces of Marche and Bourbonnais. We present a first methodological experiment using automatic speech processing tools for the extraction of verbal paradigms collected and recorded during fieldworks sessions made in situ. In order to prove the feasibility of the approach, we test it with different protocols, on good quality data, and we offer possible ways of extension for this research.
Voted-Perceptron Approach for Kazakh Morphological Disambiguation
Authors: Gulmira Tolegen, Alymzhan Toleu, Rustam Mussabayev
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Morphological Disambiguation

This paper presents an approach of voted perceptron for morphological disambiguation for the case of Kazakh language. Guided by the intuition that the feature value from the correct path of analyses must be higher than the feature value of non-correct path of analyses, we propose the voted perceptron algorithm with Viterbi decoding manner for disambiguation. The approach can use arbitrary features to learn the feature vector for a sequence of analyses, which plays a vital role for disambiguation. Experimental results show that our approach outperforms other statistical and rule-based models. Moreover, we manually annotated a new morphological disambiguation corpus for Kazakh language.
DNN-Based Multilingual Automatic Speech Recognition for Wolaytta using Oromo Speech
Authors: Martha Yifiru Tachbelie, Solomon Teferra Abate, Tanja Schultz
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

It is known that Automatic Speech Recognition (ASR) is very useful for human-computer interaction in all the human languages. However, due to its requirement for a big speech corpus, which is very expensive, it has not been developed for most of the languages. Multilingual ASR (MLASR) has been suggested to share existing speech corpora among related languages to develop an ASR for languages which do not have the required speech corpora. Literature shows that phonetic relatedness goes across language families. We have, therefore, conducted experiments on MLASR taking two language families: one as source (Oromo from Cushitic) and the other as target (Wolaytta from Omotic). Using Oromo Deep Neural Network (DNN) based acoustic model, Wolaytta pronunciation dictionary and language model we have achieved Word Error Rate (WER) of 48.34{\%} for Wolaytta. Moreover, our experiments show that adding only 30 minutes of speech data from the target language (Wolaytta) to the whole training data (22.8 hours) of the source language (Oromo) results in a relative WER reduction of 32.77{\%}. Our results show the possibility of developing ASR system for a language, if we have pronunciation dictionary and language model, using an existing speech corpus of another language irrespective of their language family.
Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of Uyghur
Authors: Ayimunishagu Abulimiti, Tanja Schultz
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling

Huge amounts of data are needed to build reliable statistical language models. Automatic speech processing tasks in low-resource languages typically suffer from lower performances due to weak or unreliable language models. Furthermore, language modeling for agglutinative languages is very challenging, as the morphological richness results in higher Out Of Vocabulary (OOV) rate. In this work, we show our effort to build word-based as well as morpheme-based language models for Uyghur, a language that combines both challenges, i.e. it is a low-resource and agglutinative language. Fortunately, there exists a closely-related rich-resource language, namely Turkish. Here, we present our work on leveraging Turkish text data to improve Uyghur language models. To maximize the overlap between Uyghur and Turkish words, the Turkish data is pre-processed on the word surface level, which results in 7.76{\%} OOV-rate reduction on the Uyghur development set. To investigate various levels of low-resource conditions, different subsets of Uyghur data are generated. Morpheme-based language models trained with bilingual data achieved up to 40.91{\%} relative perplexity reduction over the language models trained only with Uyghur data.
Basic Language Resources for 31 Languages (Plus English): The LORELEI Representative and Incident Language Packs
Authors: Jennifer Tracey, Stephanie Strassel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper documents and describes the thirty-one basic language resource packs created for the DARPA LORELEI program for use in development and testing of systems capable of providing language-independent situational awareness in emerging scenarios in a low resource language context. Twenty-four Representative Language Packs cover a broad range of language families and typologies, providing large volumes of monolingual and parallel text, smaller volumes of entity and semantic annotations, and a variety of grammatical resources and tools designed to support research into language universals and cross-language transfer. Seven Incident Language Packs provide test data to evaluate system capabilities on a previously unseen low resource language. We discuss the makeup of Representative and Incident Language Packs, the methods used to produce them, and the evolution of their design and implementation over the course of the multi-year LORELEI program. We conclude with a summary of the final language packs including their low-cost publication in the LDC catalog.
On the Exploration of English to Urdu Machine Translation
Authors: Sadaf Abdul Rauf, Syeda Abida, Noor-e- Hira, Syeda Zahra, Dania Parvez, Javeria Bashir, Qurat-ul-ain Majid
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Machine Translation is the inevitable technology to reduce communication barriers in today{'}s world. It has made substantial progress in recent years and is being widely used in commercial as well as non-profit sectors. Such is only the case for European and other high resource languages. For English-Urdu language pair, the technology is in its infancy stage due to scarcity of resources. Present research is an important milestone in English-Urdu machine translation, as we present results for four major domains including Biomedical, Religious, Technological and General using Statistical and Neural Machine Translation. We performed series of experiments in attempts to optimize the performance of each system and also to study the impact of data sources on the systems. Finally, we established a comparison of the data sources and the effect of language model size on statistical machine translation performance.
Developing a Twi (Asante) Dictionary from Akan Interlinear Glossed Texts
Authors: Dorothee Beermann, Lars Hellan, Pavel Mihaylov, Anna Struck
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Traditionally, a lexicographer identifies the lexical items to be added to a dictionary. Here we present a corpus-based approach to dictionary compilation and describe a procedure that derives a Twi dictionary from a TypeCraft corpus of Interlinear Glossed Texts. We first extracted a list of unique words. We excluded words belonging to different dialects of Akan (mostly Fante and Abron). We corrected misspellings and distinguished English loan words to be integrated in our dictionary from instances of code switching. Next to the dictionary itself, one other resource arising from our work is a lexicographical model for Akan which represents the lexical resource itself, and the extended morphological and word class inventories that provide information to be aggregated. We also represent external resources such as the corpus that serves as the source and word level audio files. The Twi dictionary consists at present of 1367 words; it will be available online and from an open mobile app.
Adapting Language Specific Components of Cross-Media Analysis Frameworks to Less-Resourced Languages: the Case of Amharic
Authors: Yonas Woldemariam, Adam Dahlgren
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We present an ASR based pipeline for Amharic that orchestrates NLP components within a cross media analysis framework (CMAF). One of the major challenges that are inherently associated with CMAFs is effectively addressing multi-lingual issues. As a result, many languages remain under-resourced and fail to leverage out of available media analysis solutions. Although spoken natively by over 22 million people and there is an ever-increasing amount of Amharic multimedia content on the Web, querying them with simple text search is difficult. Searching for, especially audio/video content with simple key words, is even hard as they exist in their raw form. In this study, we introduce a spoken and textual content processing workflow into a CMAF for Amharic. We design an ASR-named entity recognition (NER) pipeline that includes three main components: ASR, a transliterator and NER. We explore various acoustic modeling techniques and develop an OpenNLP-based NER extractor along with a transliterator that interfaces between ASR and NER. The designed ASR-NER pipeline for Amharic promotes the multi-lingual support of CMAFs. Also, the state-of-the art design principles and techniques employed in this study shed light for other less-resourced languages, particularly the Semitic ones.
Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?
Authors: Guillaume Wisniewski, S{\'e}verine Guillaume, Alexis Michaud
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, speech-recognition, Speech Recognition

Automatic Speech Recognition for low-resource languages has been an active field of research for more than a decade. It holds promise for facilitating the urgent task of documenting the world{'}s dwindling linguistic diversity. Various methodological hurdles are encountered in the course of this exciting development, however. A well-identified difficulty is that data preprocessing is not at all trivial: data collected in classical fieldwork are usually tailored to the needs of the linguist who collects them, and there is baffling diversity in formats and annotation schema, even among fieldworkers who use the same software package (such as ELAN). The tests reported here (on Yongning Na and other languages from the Pangloss Collection, an open archive of endangered languages) explore some possibilities for automating the process of data preprocessing: assessing to what extent it is possible to bypass the involvement of language experts for menial tasks of data preparation for Natural Language Processing (NLP) purposes. What is at stake is the accessibility of language archive data for a range of NLP tasks and beyond.
Manual Speech Synthesis Data Acquisition - From Script Design to Recording Speech
Authors: Atli Sigurgeirsson, Gunnar {\"O}rn{\'o}lfsson, J{\'o}n Gu{\dh}nason
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Speech Synthesis

Atli {\TH}{\'o}r Sigurgeirsson, atlithors@ru.is, Reykjavik University Gunnar Thor {\"O}rn{\'o}lfsson, gunnarthor@hi.is, {\'A}rni Magn{\'u}sson institute of Icelandic studies Dr. J{\'o}n Gu{\dh}nason, jg@ru.is In this paper we present the work of collecting a large amount of high quality speech synthesis data for Icelandic. 8 speakers will be recorded for 20 hours each. A script design strategy is proposed and three scripts have been generated to maximize diphone coverage, varying in length. The largest reading script contains 14,400 prompts and includes 87.3{\%} of all Icelandic diphones at least once and 81{\%} of all Icelandic diphones at least twenty times. A recording client was developed to facilitate recording sessions. The client supports easily importing scripts and maintaining multiple collections in parallel. The recorded data can be downloaded straight from the client. Recording sessions are carried out in a professional studio under supervision and started October of 2019. As of writing, 58.7 hours of high quality speech data has been collected. The scripts, the recording software and the speech data will later be released under a CC-BY 4.0 license.
Ow\'oksape - An Online Language Learning Platform for Lakota
Authors: Jan Ullrich, Elliot Thornton, Peter Vieira, Logan Swango, Marek Kupiec
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents Ow{\'o}ksape, an online language learning platform for the under-resourced language Lakota. The Lakota language (LakÈŸ{\'o}tiyapi) is a Siouan language native to the United States with fewer than 2000 fluent speakers. Ow{\'o}ksape was developed by The Language Conservancy to support revitalization efforts, including reaching younger generations and providing a tool to complement traditional teaching methods. This project grew out of various multimedia resources in order to combine their most effective aspects into a single, self-paced learning tool. The first section of this paper discusses the motivation for and background of Ow{\'o}ksape. Section two details the linguistic features and language documentation principles that form the backbone of the platform. Section three lays out the unique integration of cultural aspects of the Lakota people into the visual design of the application. Section four explains the pedagogical principles of Ow{\'o}ksape. Application features and exercise types are then discussed in detail with visual examples, followed by an overview of the software design, as well as the effort required to develop the platform. Finally, a description of future features and considerations is presented.