Streaming Speech Recognition on a Microcontroller: a 65.7M-Parameter Transducer in 2 and 4 Bits on the ESP32-P4
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Billal Boumedine · arXiv: 2610.38739v1 · Summary page
Why cover this story this week?
Speech recognition without a neural accelerator
A streaming speech recognizer with 65.7M parameters now runs on an ESP32-P4 microcontroller without a neural accelerator. The preprint describes a compression and engineering effort rather than a newly trained model: different weight matrices receive different numerical precision according to their sensitivity, followed by correction and rounding procedures designed to preserve the original model’s behavior. The released 71 MB of int8 weights become a 26.65 MB file, small enough for the device’s 32 MB of PSRAM.
The resulting system processes each 1.28 s audio chunk in 0.90 s, including the search that turns model outputs into words. Compression does carry an accuracy penalty: LibriSpeech word error rates are 4.34% on test-clean and 10.26% on test-other, compared with 3.94% and 9.17% for the released model. A simulator attributes the gap to compressed weights rather than integer arithmetic. The broader significance is a concrete demonstration of local speech processing under tight hardware constraints, backed by released firmware, weights and inference code. It is not yet an energy-efficiency result: energy was not measured.
Editorial context written by AI from abstracts, not independent scientific validation. The kit and its verified quotes rely on the source paper, not on this letter.
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