23 citations · 48 across the 15 of their papers we have counts for
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eess.AS2020
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models
Mohammad Zeineldeen, Albert Zeyer, Wei Zhou +3
Following the rationale of end-to-end modeling, CTC, RNN-T or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units…
eess.AS2020
A New Training Pipeline for an Improved Neural Transducer
Albert Zeyer, André Merboldt, Ralf Schlüter +1
The RNN transducer is a promising end-to-end model candidate. We compare the original training criterion with the full marginalization over all alignments, to the commonly used max…