Creating Interactive Videos for Teaching and Learning

Due to the COVID-19 pandemic outbreak, Malaysia began its lockdown on 18 March 2020, after our Prime Minister Yang Amat Berhormat Tan Sri Dato’ Haji Muhyiddin bin Haji Mohd Yassin officially announced the Movement Control Order (MCO) two days prior. At such short notice, UNIMAS took the drastic decision to embrace fully online teaching and learning (T&L) for all courses. We had to revamp our teaching and learning strategy with the most notable shift; recorded lecture videos. In a physical lecture, we can directly engage with students, however, with videos, such interaction is lost. How do we know if the students are actually paying attention or, do they understand what is presented in the video? In this article, we share our experience in using one of the hidden gems in eLEAP that is very useful for creating interactive videos. At the end of the article, we provide six simple steps that can be used as a guide

A Review on Grapheme-to-Phoneme Modelling Techniques to Transcribe Pronunciation Variants for Under-Resourced Language

A pronunciation dictionary (PD) is one of the components in an Automatic Speech Recognition (ASR) system, a system that is used to convert speech to text. The dictionary consists of word-phoneme pairs that map sound units to phonetic units for modelling and predictions. Research has shown that words can be transcribed to phoneme sequences using grapheme-to-phoneme (G2P) models, which could expedite building PDs. The G2P models can be developed by training seed PD data using statistical approaches requiring large amounts of data. Consequently, building PD for under-resourced languages is a great challenge due to poor grapheme and phoneme systems in these languages. Moreover, some PDs must include pronunciation variants, including regional accents that native speakers practice. For example, recent work on a pronunciation dictionary for an ASR in Iban, an under-resourced language from Malaysia, was built through a bootstrapping G2P method. However, the current Iban pronunciation dictionary has yet to include pronunciation variants that the Ibans practice. Researchers have done recent studies on Iban pronunciation variants, but no computational methods for generating the variants are available yet. Thus, this paper reviews G2P algorithms and processes we would use to develop pronunciation variants automatically. Specifically, we discuss data-driven techniques such as CRF, JSM, and JMM. These methods were used to build PDs for Thai, Arabic, Tunisian, and Swiss-German languages. Moreover, this paper also highlights the importance of pronunciation variants and how they can affect ASR performance

Knowledge Representation Framework for Software Requirement Specification

The need to extract correct information has become one of the main issues when analyzing the software requirement specification (SRS) documentation. The amount of gathered knowledge depends on the size of the information. However, the complexity of software systems is continuously increasing. As software systems change to more complicated systems, the information from the SRS documents may not be easily comprehended. For example, each annotation requirements tasks target the different types of information, and these tasks require the availability of experts specialized in the field. Large scale annotation tasks require multiple experts and very costly. If the number of experts is limited, annotation tasks may overwhelm the experts. The organization would not complete their objectives if they failed to manage their data because poor knowledge management affects many operations within the organization. To extract such vast information and turn it to useful knowledge, a company needs top quality software. This technology should able to input, store, and access systematically. This paper will discuss a framework based on the knowledge-based method, an attempt to improve knowledge representation. In this approach, WordNet 2.1 would be used as the knowledge source used to identify concepts represented by each word in a text from the SRS document

Social Versus Physical Distancing: Analysis of Public Health Messages at the Start of COVID-19 Outbreak in Malaysia Using Natural Language Processing

The study presents an attempt to analyse how social media netizens in Malaysia responded to the calls for ``Social Distancing'' and ``Physical Distancing'' as the newly recommended social norm was introduced to the world as a response to the COVID-19 global pandemic. The pandemic drove a sharp increase in social media platforms' use as a public health communication platform since the first wave of the COVID-19 outbreak in Malaysia in April 2020. We analysed thousands of tweets posted by Malaysians daily between January 2020 and August 2021 to determine public perceptions and interactions patterns. The analysis focused on positive and negative reactions and the interchanges of uses of the recommended terminologies ``social distancing'' and ``physical distancing''. Using linguistic analysis and natural language processing, findings dominantly indicate influences from the multilingual and multicultural values held by Malaysian netizens, as they embrace the concept of distancing as a measure of global public health safety

Preliminary Evaluation of Convolutional Neural Network Acoustic Model for Iban Language Using NVIDIA NeMo

For the past few years, artificial neural networks (ANNs) have been one of the most common solutions relied upon while developing automated speech recognition (ASR) acoustic models. There are several variants of ANNs, such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). A CNN model is widely used as a method for improving image processing performance. In recent years, CNNs have also been utilized in ASR techniques, and this paper investigates the preliminary result of an end-to-end CNN-based ASR using NVIDIA NeMo on the Iban corpus, an under-resourced language. Studies have shown that CNNs have also managed to produce excellent word error (WER) rates for the acoustic model on ASR for speech data. Conversely, results and studies concerned with under-resourced languages remain unsatisfactory. Hence, by using NVIDIA NeMo, a new ASR engine developed by NVIDIA, the viability and the potential of this alternative approach are evaluated in this paper. Two experiments were conducted: the number of resources used in the works of our ASR’s training was manipulated, as was the internal parameter of the engine used, namely the epochs. The results of those experiments are then analyzed and compared with the results shown in existing papers

Design of a Transcription Tool for the Kelabit Community of Bario, Sarawak

This paper describes the design of a transcription tool developed for the Kelabit community of Bario, Sarawak based on the community’s requirements and feedbacks. Transcribing is the process of making a full written copy of spoken or dictated material. Using this transcription tool, users can listen to audio files of recordings and proceed to do the transcribing within the tool itself. Afterwards, they can save the transcription into text files for future uses. Users can control the audio file while it is playing, and do updating or editing to the transcription. This project was carried out on the motivation to assist in language preservation works as Kelabit is an underresourced language