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publications
WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs
Published in IEEE Access, 2023
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Recommended citation: K. W. Kun et al., "WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs," in IEEE Access, vol. 11, pp. 48901-48911, 2023, doi: 10.1109/ACCESS.2023.3276319.
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An Isotropy Analysis for Self-Supervised Acoustic Unit Embeddings on the Zero Resource Speech Challenge 2021 Framework
Published in 2023 International Conference on Acoustics, Speech, and Signal Processing, 2023
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Recommended citation: J. Chen and S. Sakti, "An Isotropy Analysis for Self-Supervised Acoustic Unit Embeddings on the Zero Resource Speech Challenge 2021 Framework," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10095119.
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Data Selection using Spoken Language Identification for Low-Resource and Zero-Resource Speech Recognition
Published in 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2024
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Recommended citation: J. Chen, C. Chu, S. Li and T. Kawahara, "Data Selection using Spoken Language Identification for Low-Resource and Zero-Resource Speech Recognition," 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Macau, Macao, 2024, pp. 1-6, doi: 10.1109/APSIPAASC63619.2025.10848811.
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Loasr-bench: Evaluating large speech language models on low-resource automatic speech recognition across language families
Published in Signal Processing Letters, 2026
Large language models (LLMs) have driven substantial advances in speech language models (SpeechLMs), yielding strong performance in automatic speech recognition (ASR) under high-resource conditions. However, existing benchmarks predominantly focus on high-resource languages, leaving the ASR behavior of SpeechLMs in low-resource languages insufficiently understood. This gap is critical, as practical ASR systems must reliably support low-resource languages and generalize across diverse language families, and it directly hinders the deployment of SpeechLM-based ASR in real-world multilingual scenarios. As a result, it is essential to evaluate SpeechLMs on low-resource languages to ensure their generalizability across different language families. To address this problem, we propose LoASR-Bench, a comprehensive benchmark designed to evaluate low-resource automatic speech recognition…
Recommended citation: Chen, J., Gao, X., Tatsuya, K., & Chen, N. F. (2026). Loasr-bench: Evaluating large speech language models on low-resource automatic speech recognition across language families. IEEE Signal Processing Letters.
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Language-Aware Routing in Mixtures of Experts for Low-Resource Automatic Speech Recognition
Published in 2026 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2026
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