Apple revisits ASR error correction with compact seq2seq models

Apple's research introduces a compact seq2seq model for ASR error correction with 15x fewer parameters than LLMs, achieving 1.5/3.3% WER on LibriSpeech test-clean/other and outperforming LLMs. The model uses correction-first decoding with ASR acoustic scores and generalizes across CTC, Seq2seq, and Transducer architectures, avoiding latency and hallucination issues common with LLMs.