  This paper proposes Transducers with Pronunciation-aware Embeddings (PET).
Unlike conventional Transducers where the decoder embeddings for different
tokens are trained independently, the PET model's decoder embedding
incorporates shared components for text tokens with the same or similar
pronunciations. With experiments conducted in multiple datasets in Mandarin
Chinese and Korean, we show that PET models consistently improve speech
recognition accuracy compared to conventional Transducers. Our investigation
also uncovers a phenomenon that we call error chain reactions. Instead of
recognition errors being evenly spread throughout an utterance, they tend to
group together, with subsequent errors often following earlier ones. Our
analysis shows that PET models effectively mitigate this issue by substantially
reducing the likelihood of the model generating additional errors following a
prior one. Our implementation will be open-sourced with the NeMo toolkit.
