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Author: search.filters.author.Oo, Thazin MyintHas files: No

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    ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers Using Phonetic Features
    (2025-01-01)
    Lin, Ye Bhone
    ;
    Aung, Thura
    ;
    Thu, Ye Kyaw
    ;
    Oo, Thazin Myint
    This paper investigates sequence-to-sequence Transformer models for automatic speech recognition (ASR) error correction in low-resource Burmese, focusing on different feature integration strategies including IPA and alignment information. To our knowledge, this is the first study addressing ASR error correction specifically for Burmese. We evaluate five ASR backbones and show that our ASR Error Correction (AEC) approaches consistently improve word- and character-level accuracy over baseline outputs. The proposed AEC model, combining IPA and alignment features, reduced the average WER of ASR models from 51.56 to 39.82 before augmentation (and 51.56 to 43.59 after augmentation) and improving chrF++ scores from 0.5864 to 0.627, demonstrating consistent gains over the baseline ASR outputs without AEC. Our results highlight the robustness of AEC and the importance of feature design for improving ASR outputs in low-resource settings.
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    Enhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-Tuning
    (2025-01-01)
    Aung, Thura
    ;
    Kyaw, Eaint Kay Khaing
    ;
    Thu, Ye Kyaw
    ;
    Oo, Thazin Myint
    ;
    Supnithi, Thepchai
    In low-resource languages like Burmese, classification tasks often fine-tune only the final classification layer, keeping pre-trained encoder weights frozen. While Multi-Layer Perceptrons (MLPs) are commonly used, their fixed non-linearity can limit expressiveness and increase computational cost. This work explores Kolmogorov-Arnold Networks (KANs) as alternative classification heads, evaluating Fourierbased FourierKAN, Spline-based EfficientKAN, and Grid-based FasterKAN-across diverse embeddings including TF-IDF, fastText, and multilingual transformers (mBERT, Distil-mBERT). Experimental results show that KAN-based heads are competitive with or superior to MLPs. EfficientKAN with fast-Text achieved the highest F1-score (0.928), while FasterKAN offered the best trade-off between speed and accuracy. On transformer embeddings, EfficientKAN matched or slightly outperformed MLPs with mBERT (0.917 F1). These findings highlight KANs as expressive, efficient alternatives to MLPs for lowresource language classification.
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    Myanmar Text Grade-Level Prediction Using Statistical and Linguistics Features
    (2025-01-01)
    Wai, Khaing Hsu
    ;
    Nwe, Hlaing Myat
    ;
    Aung, Thura
    ;
    Thu, Kaung Khant Si
    ;
    Mon, Hsu Yee
    Readability assessment supports curriculum design and adaptive learning by estimating the difficulty of a text for readers at different proficiency levels. However, research on text readability for the Myanmar language remains unexplored, mainly due to the absence of labeled resources and established computational approaches. This paper presents the first comprehensive study on Myanmar text readability classification. We construct a grade-annotated corpus from official Myanmar school textbooks (Grades 1-12) and extract linguistic, statistical, and Myanmar-specific indicators. We then evaluate regression and classification baselines, text-only embeddings, and ensembles. We also adapt three classic readability formulas (LIX, Dale-Chall, Flesch) to Myanmar and empirically show that their score distributions overlap heavily across educational levels. Experimental results show that ensemble-based models achieved the best performance in predicting grade levels, demonstrating the effectiveness of our feature design and modeling framework. This work introduces the first readability dataset, modeling approaches, and benchmark results for the Myanmar language, providing a strong foundation for future research in readability prediction, low-resource Natural language processing (NLP), and educational text analysis.
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    NECTEC’s Participation in WAT-2021
    (2021-01-01)
    Hlaing, Zar Zar
    ;
    Thu, Ye Kyaw
    ;
    Oo, Thazin Myint
    ;
    San, Mya Ei
    ;
    Usanavasin, Sasiporn
    In this paper, we report the experimental results of Machine Translation models conducted by a NECTEC team (Team-ID: NECTEC) for the WAT-2021 Myanmar-English translation task (Nakazawa et al., 2021). Basically, our models are based on neural methods for both directions of English-Myanmar and Myanmar-English language pairs. Most of the existing Neural Machine Translation (NMT) models mainly focus on the conversion of sequential data and do not directly use syntactic information. However, we conduct multi-source neural machine translation (NMT) models using the multilingual corpora such as string data corpus, tree data corpus, or POS-tagged data corpus. The multi-source translation is an approach to exploit multiple inputs (e.g. in two different formats) to increase translation accuracy. The RNN-based encoder-decoder model with attention mechanism and transformer architectures have been carried out for our experiment. The experimental results showed that the proposed models of RNN-based architecture outperform the baseline model for the English-to-Myanmar translation task, and the multi-source and sharedmulti-source transformer models yield better translation results than the baseline.