Progressive Alignment Objectives for Aligner-Encoder based ASR
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Electrical Engineering and Systems Science > Audio and Speech Processing
Title:Progressive Alignment Objectives for Aligner-Encoder based ASR
Abstract:Aligner-Encoders are recently proposed seq2seq end-to-end ASR models that replace decoder attention by predicting the uth token directly from the u-th encoder position, so the encoder must learn the alignment internally without cross-attention or a transducer lattice. In practice, this alignment often forms abruptly in the upper layers, making training sensitive and brittle on long utterances. We propose InterAligner, which adds an intermediate Aligner objective so alignment can form progressively across depth, together with an intermediate CTC loss (InterCTC) to stabilize optimization. On LibriSpeech with a 17-layer Conformer, a final-only Aligner reaches 5.0/7.8 WER (test-clean/other). InterCTC improves to 3.4/6.0, and InterAligner further reduces WER to 3.1/5.6 with the largest gains on long utterances.
| Comments: | Accepted to Interspeech 2026 |
| Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2606.24147 [eess.AS] |
| (or arXiv:2606.24147v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24147
arXiv-issued DOI via DataCite (pending registration)
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