Before revision: Common end-to-end models like CTC or encoder-decoder-attention models use characters or subword units like BPEas the output labels. We do systematic comparisons between grapheme-based and phoneme-based output labels .
After revision: Common end-to-end models like CTC or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units based on e.g. byte-pair encoding (BPE). The mapping from pronunciation to spelling is learned completely from data. In contrast to this, classical approaches to ASR employ secondary knowledge sources in the form of phoneme lists to define phonetic output labels and pronunciation lexica. In this work, we do a systematic comparison between grapheme- and phoneme-based output labels .