# LightBeam: An Accurate and Memory-Efficient CTC Decoder for Speech Neuroprostheses Source: [LightBeam: An Accurate and Memory-Efficient CTC Decoder for Speech Neuroprostheses]() Collection index: [llms.txt]() Data updated: 2026-03-18T05:00:04.467Z Authors: Ebrahim Feghhi, Junlin Hu, Nima Hadidi, Jonathan C. Kao Published: 2026-03-14T16:06:15.000Z ## Abstract 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 ${\\sim}$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 ${\\sim}$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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Brain-to-Text Decoding on B2T '24 (test)](): Brain-to-Text Decoding; B2T '24 (test); WER: 9.37. - [Brain-to-Text Decoding on B2T '25 (test)](): Brain-to-Text Decoding; B2T '25 (test); Public WER: 5.77. ## Original sources and implementation [Original paper]() [PDF]()