E-Gaborformer: Strictly Causal Speech Recognizer with Constant Streaming Memory
arXiv cs.LG (Machine Learning) RSS Feed · Nir Krakowski, Tamir Cohen, Ohad Zohar, Chen Zarfati · Published on Readzer
Summary
A new arXiv paper presents E-Gaborformer, a strictly causal encoder combining Gated DeltaNet-2 recurrent layers with sliding-window attention and fixed causal Gabor atoms. It maintains a constant streaming state of about 19 MB per call at production scale, versus over 1 GB for a same-size causal-attention encoder after ten minutes. In production, a 0.6B-parameter Hebrew and English streaming transducer built on it achieves 28–31% lower WER than NVIDIA's Nemotron 3.5 ASR Streaming on English voice-agent traffic, and up to 20% lower Hebrew WER than the previous system, with 2.6x faster training via a two-class RNN-T lattice formulation.
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Why it matters by industry
- Technology
Relevant to software and digital infrastructure: introduces a new speech recognition architecture with constant memory and lower WER, directly impacting voice-agent systems and streaming ASR deployment.