Papers by Yosuke Higuchi
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026.acl-long)
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Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz Tandazo, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Éric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar-Corrales, null Surya, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux
| Challenge: | Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability and downstream spoken language modeling scores . current self-supervised learning models require thousands of hours of training data to learn meaningful linguistic representations. |
| Approach: | They propose a bi-level optimization framework for rapid adaptation of speech units to new languages using minimal unlabeled data. |
| Outcome: | The proposed model achieves rapid gains in phonemic discriminability and spoken language modeling scores . it surpasses in-domain toplines after training on less than 1h of target-language audio . |
CTC Alignments Improve Autoregressive Translation (2023.eacl-main)
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Brian Yan, Siddharth Dalmia, Yosuke Higuchi, Graham Neubig, Florian Metze, Alan W Black, Shinji Watanabe
| Challenge: | Connectionist Temporal Classification (CTC) is widely used for automatic speech recognition (ASR) but lags behind attentional decoder approaches in terms of translation quality. |
| Approach: | They propose to use a CTC/attention framework to validate this hypothesis by modifying the Hybrid CTC-Attention model proposed for automatic speech recognition to support text-to-text translation (MT) and speech-totext translation. |
| Outcome: | The proposed model outperforms pure-attention baselines across six translation tasks. |
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model (2022.findings-emnlp)
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| Challenge: | Existing approaches to connectionist temporal classification (CTC) are based on pre-trained language models (LMs) |
| Approach: | They propose a formulation of connectionist temporal classification that relaxes the conditional independence assumptions used in conventional CTC and incorporates linguistic knowledge through explicit output dependency. |
| Outcome: | The proposed model improves over conventional approaches across variations in speaking styles and languages while maintaining CTC’s training efficiency. |