Papers by Motoi Omachi
End-to-end ASR to jointly predict transcriptions and linguistic annotations (2021.naacl-main)
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| Challenge: | Existing models generate audio transcripts by sequentially producing likely graphemes, or multi-graphemic units, from which lexical items of a language can be recovered. |
| Approach: | They propose a Transformer-based sequence-to-sequence model for automatic speech recognition that can produce high-quality transcriptions and linguistic annotations. |
| Outcome: | The proposed model can produce high-quality transcriptions and linguistic annotations on Japanese and English audio datasets. |