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

Speech Enhancement Techniques for Noisy Speech in Real World Environments

Communication between computer andhuman has become increasingly popular in todayworld. Investigation of human emotion importance isalso growing in several domains. But under realworld condition, speech signal is often, corruptedwith several noise types and the accuracy ofrecognition is degraded from these noisy signal.Therefore this paper focuses on the speechenhancement techniques to develop emotionrecognition system for the noisy signal in the realworld environment. The various popularenhancement techniques are analyzed by adding thebackground noise to the clean signal using variousSNR. To test the accuracy of the system, the widelyused MFCC signal features are against with the SVMclassifier. Results after enhancing were compared tothat noisy signal and that clean signal to measure thesystem performance. The experimental results showthe best performance algorithm and all enhancementalgorithms improve the emotion recognition systemperformance under various SNRs level of real worldbackground noise

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

Evaluation of the host response of lowland and upland rice varieties from Myanmar to the rice root-knot nematode Meloidogyne graminicola

The rice root-knot nematode Meloidogyne graminicola is considered one of the most potentially important nematode pathogens of rice, especially in South and Southeast Asia, in a range of rice production systems. Identification of M. graminicola-resistant or -tolerant rice varieties will enable breeding programmes to develop rice varieties which are able to limit yield losses caused by this nematode species. The host response to M. graminicola infection of 15 lowland rice varieties and 9 upland rice varieties, which are being grown in the summer-irrigated lowland and rainfed upland rice ecosystems in Myanmar, was evaluated in two experiments under screenhouse conditions. The lowland rice experiment was carried out under intermittently flooded conditions in a clay loam soil (i.e. simulating the summer-irrigated lowland rice ecosystem) and the upland rice experiment was carried out at field capacity in a sandy loam soil (i.e. simulating the monsoon rainfed upland rice ecosystem). None of the15 lowland and 9 upland rice varieties were resistant to M. graminicola infection although differences in susceptibility and sensitivity were observed. Six (or 40%) out of the 15 lowland varieties examined were classified as less susceptible (LS) to M. graminicola infection, five (or 33.3%) as moderately susceptible (MS) while four (or 26.7%) as highly susceptible (HS). One (or 11.1%) out of the nine upland varieties examined was classified as LS to M. graminicola infection, three (or 33.3%) as MS while five (or 55.6%) as HS. Five (or 33.3%) out of the 15 lowland varieties examined were classified as either less sensitive or tolerant to M. graminicola infection. One (or 11.1%) out of the nine upland varieties examined was classified as tolerant to M. graminicola infection. This study offers interesting information for the farmer regarding which rice variety should be grown in M. graminicola-infested fields under either lowland or upland conditions. © 2013 © 2013 Taylor & Francis.status: publishe

Integrated Women's Health Programme (IWHP): A cross-sectional study of prevalence & correlates for sarcopenia in midlife Singaporean women

Annual Meeting of the American-Society-for-Bone-and-Mineral-Research33186-18

Population dynamics of Meloidogyne graminicola and Hirschmanniella oryzae in a double rice-cropping sequence in the lowlands of Myanmar

The rice root-knot nematode, Meloidogyne graminicola, and the rice root nematode, Hirschmanniella oryzae, are considered potentially important nematode pathogens in lowland rice. A study was undertaken from December 2009 until December 2010 in the Ayeyarwady River Delta, the major lowland rice-producing area of Myanmar, to monitor the population dynamics of M. graminicola and H. oryzae in a naturally infested field. Root samples of the two rice varieties Yatanartoe and Taungpyan that are commonly cultivated in double rice-cropping sequences in Myanmar and represent irrigated and rainfed lowland rice varieties, respectively, were obtained for nematode analysis. During the summer-irrigated rice-growing season the root population density of second-stage juveniles (J2) of M. graminicola showed two distinct peaks – at the maximum tillering stage of the rice plants in January and at the heading stage of the rice plants in March 2010. With the onset of the monsoon rains, the J2 population densities in the roots of ratoon rice plants gradually decreased in May. During the rainfed monsoon rice-growing season, very low population densities of M. graminicola J2 were detected in the roots of rice plants, while the root population density of H. oryzae juveniles and adults showed two distinct peaks – at the maximum tillering stage of the rice plants in August and at the heading stage of the rice plants in October 2010. With the onset of the dry season, population density of H. oryzae in the roots reached the lowest density at harvest in November. Root galling caused by M. graminicola followed the same trend as the J2 population densities throughout the irrigated season. No root galls were observed during the monsoon season. Our results can be used for practical purposes aimed at a better management of both M. graminicola and H. oryzae.http://booksandjournals.brillonline.com/content/journals/10.1163/15685411-0000271

String to Tree and Tree to String Statistical Machine Translation for Myanmar Language

This paper contributes the first publishedevaluation of the quality of string-to-tree (S2T)and tree-to-string (T2S) statistical machinetranslation methods between Myanmar andChinese, English, French, German in bothdirections. The performance of machinetranslation was automatically measured in termsof BLEU and RIBES scores for all experiments.In addition we performed a comparative study ofthe performance of phrase-based statisticalmachine translation (PBSMT) and T2S usinghuman judgment. We found that the resultsobtained using the BLEU automatic evaluationmetric were misleading and found that the T2Sapproach is suitable for distant languages toMyanmar machine translation