Before revision:  We do systematic comparisons between grapheme-based and phoneme-based output labels . These can be single phonemes without context (~40 labels), or multiple phonemes together in one output label, such that we get phoneme-based subwords. For this purpose, we introduce phoneme-based BPE labels . In further experiments, we extend the phoneme set by auxiliary units to be able to discriminate homophones(different words with same pronunciation).
After revision:  We do systematic comparisons between grapheme-based and phoneme-based output labels for an encoder-decoder-attention ASR model. We investigate the use of single phonemes as well as BPE-based phoneme groups as output labels of our model. To preserve a simplified and efficient decoder design, we also extend the phoneme set by auxiliary units to be able to discriminate homophones(different words with same pronunciation).