Posts by Collection

Portfolio item number 1

Short description of portfolio item number 1

Portfolio item number 2

Short description of portfolio item number 2

WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs

Published in IEEE Access, 2023

The contents above will be part of a list of publications, if the user clicks the link for the publication than the contents of section will be rendered as a full page, allowing you to provide more information about the paper for the reader. When publications are displayed as a single page, the contents of the above “citation” field will automatically be included below this section in a smaller font.

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.
Download Paper

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

The contents above will be part of a list of publications, if the user clicks the link for the publication than the contents of section will be rendered as a full page, allowing you to provide more information about the paper for the reader. When publications are displayed as a single page, the contents of the above “citation” field will automatically be included below this section in a smaller font.

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.
Download Paper

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

The contents above will be part of a list of publications, if the user clicks the link for the publication than the contents of section will be rendered as a full page, allowing you to provide more information about the paper for the reader. When publications are displayed as a single page, the contents of the above “citation” field will automatically be included below this section in a smaller font.

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.
Download Paper

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.
Download Paper

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

The contents above will be part of a list of publications, if the user clicks the link for the publication than the contents of section will be rendered as a full page, allowing you to provide more information about the paper for the reader. When publications are displayed as a single page, the contents of the above “citation” field will automatically be included below this section in a smaller font.

Recommended citation:
Download Paper