A promising pathway for restoring communication in patients with dysarthria and anarthria is speech neuroprostheses, which directly decode speech from cortical neural activity. Two benchmarks, Brain-to-Text '24 and '25, released intracranial recordings from patients with dysarthria along with a baseline algorithm trained with Connectionist Temporal Classification (CTC). Despite significant innovation on these benchmarks, all leading published prior work relies on a WFST-based CTC decoder that requires ∼320 GB of RAM. These memory requirements limit accessibility for both patients and researchers. Here, we propose LightBeam, a non-WFST based CTC decoder that requires only ∼10 GB of RAM and achieves state-of-the-art performance on both benchmarks. LightBeam achieves this by integrating an LLM into the beam-search process via delayed fusion, obviating the prior need for using a large N-gram LM. LightBeam is implemented in Python and is open-source.
@article{arxiv.2603.14002,
title = {LightBeam: An Accurate and Memory-Efficient CTC Decoder for Speech Neuroprostheses},
author = {Ebrahim Feghhi and Junlin Hu and Nima Hadidi and Jonathan C. Kao},
journal= {arXiv preprint arXiv:2603.14002},
year = {2026}
}