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Residual Expert Routing for Text-Only Domain Adaptation of Factorized Transducer ASR

2026-10-23 13:48:33
Shashi Kumar, Esa\'u Villatoro-Tello, Thibault Ba\~neras-Roux, Sergio Burdisso, Petr Motlicek, Kadri Hacioglu, Andreas Stolcke

Abstract

Fine-tuning the non-blank predictor of a factorized transducer on target-domain text improves recognition on that domain but degrades performance on the source domain. A single interpolation weight between base and adapted predictors cannot resolve this: the adapted predictor helps at some prefixes and hurts at others, often within one utterance. We propose conservative residual routing: the adapted predictor becomes a residual correction to the base, and a 16k-parameter gate decides at each prefix how much of it to admit. Closing the gate reproduces the base exactly. On a 40-hour conversational corpus, routing retains essentially all of the text-only gain on two held-out domains (19.89 to 16.74, 20.91 to 16.21 WER) while leaving source-domain WER unchanged (12.90 to 12.93); the adapted predictor alone costs 1.08 WER. Routing needs no per-domain tuning on target-domain audio. Analysis reveals the gate does not behave as a simple domain-level switch: it modulates intervention through the disagreement between the two predictors, suppressing it in-domain and amplifying it out-of-domain.

Abstract (translated)

URL

https://arxiv.org/abs/2610.32637

PDF

https://arxiv.org/pdf/2610.32637.pdf


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