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arXiv · 2508.08938

DeCRED: Decoder-Centric Regularization for Encoder-Decoder Based Speech Recognition

Alexander Polok·Santosh Kesiraju·Karel Beneš·Bolaji Yusuf·Lukáš Burget·Jan Černocký
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Abstract

This paper presents a simple yet effective regularization for the internal language model induced by the decoder in encoder-decoder ASR models, thereby improving robustness and generalization in both in- and out-of-domain settings. The proposed method, Decoder-Centric Regularization in Encoder-Decoder (DeCRED), adds auxiliary classifiers to the decoder, enabling next token prediction via intermediate logits. Empirically, DeCRED reduces the mean internal LM BPE perplexity by 36.6% relative to 11 test sets. Furthermore, this translates into actual WER improvements over the baseline in 5 of 7 in-domain and 3 of 4 out-of-domain test sets, reducing macro WER from 6.4% to 6.3% and 18.2% to 16.2%, respectively. On TEDLIUM3, DeCRED achieves 7.0% WER, surpassing the baseline and encoder-centric InterCTC regularization by 0.6% and 0.5%, respectively. Finally, we compare DeCRED with OWSM v3.1 and Whisper-medium, showing competitive WERs despite training on much less data with fewer parameters.

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Alexander Polok, Santosh Kesiraju, Karel Beneš, Bolaji Yusuf, Lukáš Burget, Jan Černocký. 2025-08-12. DeCRED: Decoder-Centric Regularization for Encoder-Decoder Based Speech Recognition. https://arxiv.org/abs/2508.08938

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