Adding auxiliary left and right phoneme prediction heads to full-sum ASR training improves word error rates, especially on 300h Switchboard, and enables full-sum-only factored hybrid HMM training without external alignments.
H MM vs. CTC for Automatic Speech Recognition: Comparison Based on F ull- Sum Training from Scratch,
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Right Label Context in End-to-End Training of Time-Synchronous ASR Models
Adding auxiliary left and right phoneme prediction heads to full-sum ASR training improves word error rates, especially on 300h Switchboard, and enables full-sum-only factored hybrid HMM training without external alignments.