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Mark A. Hasegawa-Johnson

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#machine learning Preprint Sep 2026

BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment

Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a heartbeat and the number of beats in each patch shifts with heart rate. This matters most for infants, whose heart rates are higher and whose ECG differs from the adult...

Mohammad Nur Hossain Khan, M. Krafczyk, Beverly G. Bolster et al. · 0 citations
#natural language process... Preprint Sep 2026

Learning New Words from Unlabeled Test Data in Automatic Speech Recognition

It is demonstrated that the CTC-weighted language model log likelihood ratio can be interpreted as the KLD between the unknown correct ASR and the unsupervised learned ASR, and that, using a Pinsker bound, the square root of KLD can be interpreted as an upper bound on the total variation distance between the true and e...

Meng-Qi Wang, Mark A. Hasegawa-Johnson, Hao-Long Zheng et al. · 0 citations

FSA-GRPO: Teaching Auditory LLMs to Use Few-shot Demonstrations

This work introduces Few-Shot Aware GRPO (FSA-GRPO), an RL-based post-training recipe that uses a specially designed reward to encourage the model to leverage few-shot demonstrations, thereby strengthening its few-shot adaptation ability.

Haolong Zheng, Siyin Wang, Xulin Fan et al. · 1 citation
Preprint Aug 2026

Unsupervised Speech Recognition at the Syllable Level

This paper introduces a syllable-level UASR framework based on masked language modeling, which avoids the need for G2P and the instability of GAN-based methods and generalizes effectively to low-resource languages that have remained particularly difficult for prior methods.

Liming Wang, Kai-Wei Chang, K. Kashino et al. · 0 citations

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