UCSY-SC1: A Myanmar speech corpus for automatic speech recognition

This paper introduces a speech corpus which is developed for Myanmar Automatic Speech Recognition (ASR) research. Automatic Speech Recognition (ASR) research has been conducted by the researchers around the world to improve their language technologies. Speech corpora are important in developing the ASR and the creation of the corpora is necessary especially for low-resourced languages. Myanmar language can be regarded as a low-resourced language because of lack of pre-created resources for speech processing research. In this work, a speech corpus named UCSY-SC1 (University of Computer Studies Yangon - Speech Corpus1) is created for Myanmar ASR research. The corpus consists of two types of domain: news and daily conversations. The total size of the speech corpus is over 42 hrs. There are 25 hrs of web news and 17 hrs of conversational recorded data.The corpus was collected from 177 females and 84 males for the news data and 42 females and 4 males for conversational domain. This corpus was used as training data for developing Myanmar ASR. Three different types of acoustic models  such as Gaussian Mixture Model (GMM) - Hidden Markov Model (HMM), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) models were built and compared their results. Experiments were conducted on different data  sizes and evaluation is done by two test sets: TestSet1, web news and TestSet2, recorded conversational data. It showed that the performance of Myanmar ASRs using this corpus gave satisfiable results on both test sets. The Myanmar ASR  using this corpus leading to word error rates of 15.61% on TestSet1 and 24.43% on TestSet2

Statistical Machine Translation between Myanmar Sign Language and Myanmar Written Text

This paper contributes the first evaluation of the quality of automatic translation between Myanmar sign language (MSL) and Myanmar written text, in both directions. Our developing MSL-Myanmar parallel corpus was used for translations and the experiments were carried out using three different statistical machine translation (SMT) approaches: phrase-based, hierarchical phrase-based, and the operation sequence model. In addition, three different segmentation schemes were studies, these were syllable segmentation, word segmentation and sign unit based word segmentation. The results show that the highest quality machine translation was attained with syllable segmentations for both MSL and Myanmar written text

Development of Natural Language Processing based Communication and Educational Assisted Systems for the People with Hearing Disability in Myanmar

Information and communication technologies (ICTs) provide people with disabilities to better integrate socially and economically into their communities by supporting access to information and knowledge, learning and teaching situations, personal communication and interaction. Our research purpose is to develop systems that will provide communication and educational assistance to persons with hearing disability using Natural Language Processing (NLP). In this paper, we present corpus building for Myanmar sign language (MSL), Machine Translation (MT) between MSL, Myanmar written text (MWT) and Myanmar SignWriting (MSW) and two Fingerspelling keyboard layouts for Myanmar SignWriting. We believe that the outcome of this research is useful for educational contents and communication between hearing disability and general people

Malaria epidemiology in central Myanmar: identification of a multi-species asymptomatic reservoir of infection.

BACKGROUND: The spread of artemisinin-resistant Plasmodium falciparum is a global health concern. Myanmar stands at the frontier of artemisinin-resistant P. falciparum. Myanmar also has the highest reported malaria burden in Southeast Asia; it is integral in the World Health Organization's plan to eliminate malaria in Southeast Asia, yet few epidemiological data exist for the general population in Myanmar. METHODS: This cross-sectional, probability household survey was conducted in Phyu township, Bago Region (central Myanmar), during the wet season of 2013. Interviewers collected clinical and behavioural data, recorded tympanic temperature and obtained dried blood spots for malaria PCR and serology. Plasmodium falciparum positive samples were tested for genetic mutations in the K13 region that may confer artemisinin resistance. Estimated type-specific malaria PCR prevalence and seroprevalence were calculated, with regression analysis to identify risk factors for seropositivity to P. falciparum. Data were weighted to account for unequal selection probabilities. RESULTS: 1638 participants were sampled (500 households). Weighted PCR prevalence was low (n = 41, 2.5%) and most cases were afebrile (93%). Plasmodium falciparum was the most common species (n = 19. 1.1%) and five (26%) P. falciparum samples harboured K13 mutations. Plasmodium knowlesi was detected in 1.0% (n = 16) and Plasmodium vivax was detected in 0.4% (n = 7). Seroprevalence was 9.4% for P. falciparum and 3.1% for P. vivax. Seroconversion to P. falciparum was 0.003/year in the whole population, but 16-fold higher in men over 23 years old (LR test p = 0.016). DISCUSSION: This is the first population-based seroprevalence study from central Myanmar. Low overall prevalence was discovered. However, these data suggest endemic transmission continues, probably associated with behavioural risk factors amongst working-age men. Genetic mutations associated with P. falciparum artemisinin resistance, the presence of P. knowlesi and discrete demographic risk groups present opportunities and challenges for malaria control. Responses targeted to working-age men, capable of detecting sub-clinical infections, and considering all species will facilitate malaria elimination in this setting

The spiritual and mental health assessment of social workers working for Internally Displaced Persons during Covid-19 in Myanmar

This study applies the spiritual assessment viewpoint toanalyze the responses of participants who are working for InternallyDisplaced Persons (IDPs) in Kachin and Northern Shan State,Myanmar. It was an online survey of workers' work experience in April2022. The analysis applies a quantitative research method to explorethe spirituality of social workers who are working in conflict-affectedareas where works are stressful, and the guarantee of security isuncertain. To analyze participants’ spirituality, in quantitative method,the form of the Spirituality Assessment of Worker Working for IDPs(SASSIDPs) is designed as three dimensions of SASSIDPs: Healthyself-awareness (HSA), healthy relationship (HR), and healthy feeling(HF). The results are conducted with the Social Science StatisticsSoftware (SPSS) 22.0 for the statistical analysis. Most of theparticipants are female (66%) and 34% are male. The results of in threedimensions of the SASSIDPs are different from their socialcharacteristics: in gender, males and females are significantly differentin HF of SASSIDPs (t=-3.21**), female is higher than male; in termsof religion/faith, group of “Christianity” is significantly different fromthe group of “non-Christian” in HF(t=-2.833**), and “non-Christianity”group gets higher agree than “Christianity” group; in term of workingyears, people who have been working below 2 years has significantlydifferent from 3 years above in HSA (t=2.918**); in term of age, thereis no difference among the different age groups

Unsupervised Dependency Parsing for Myanmar Language using Part-of-Speech Information

In this paper, we present the preliminary experimentsof unsupervised dependency parsing on rawsegmented and part-of-speech (POS) tagged corpusof Myanmar language. This experiment is aimed tosupport building treebank and get ann-otated corpuswith dependency structures of My-anmar words. Thereferenced word dependency schemes are alsoexplained. We present the expr-imental results ontrees of unsupervised parsed annotated corpus interms of unlabeled and labe-led attachment scores(UAS and LAS) by UDPi-pe 89.79 % and 85.56% fortest and 98.25% and 97.89% for trained datarespectively

Building HMM-SGMM Continuous Automatic Speech Recognition on Myanmar Web News

Myanmar language is a tonal and analyticlanguage. It can be considered as an under-resourcedlanguage because of its linguistic resource availability.Therefore, speech data collection is a very challengingtask in building Myanmar automatic speechrecognition. Today a lot of speech data are freelyavailable on the Internet and we can collect it easily.Therefore, in this system, we take the advantages ofInternet and we use daily news from the Web inbuilding our speech corpus. In this paper, we willpresent about the task of data collection, the effect ofAutomatic Speech Recognition (ASR) performanceaccording to amount of training data, language modelsize and error analysis of the experimental result. Theexperiments will be developed using Hidden MarkovModel (HMM) with Gaussian Mixture Model (GMM)and Subspace Gaussian Mixture Model (SGMM). As aresult, using our developed 5 hours training data, thissystem achieves word error rate (WER) of 7.6% onclose test data and 31.9% on open test data withHMM-SGMM