arXiv:2606.24147v1 Announce Type: cross 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
The continuous evolution of end-to-end ASR models is driving research into more stable and efficient training methods, addressing current limitations in handling long utterances.
Improved alignment and training stability for ASR models will lead to more robust and accurate speech recognition, broadening its applicability and reliability in various AI applications.
This research introduces a method to make Aligner-Encoder ASR models more stable and less brittle, potentially accelerating their adoption for complex speech tasks.
- · AI developers
- · Speech recognition companies
- · Cloud providers
- · Researchers in NLP/ASR
ASR models become more reliable for longer and more complex audio inputs.
Enhanced ASR capabilities could lead to more sophisticated voice interfaces and automated transcription services.
Improved speech recognition forms a foundational layer for more advanced AI agents capable of nuanced human-computer interaction, potentially impacting white-collar workflows.
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Read at arXiv cs.CL