Investigations on Phoneme-Based End-To-End Speech Recognition
Table 1: On Switchboard 300h, comparing phoneme and grapheme and whole word models. Using simple decoding, using beam size 12. All phoneme models here have word-disambiguate symbols. Phoneme single is with end-of-word (EOW) symbol. Grapheme single is with whitespace, which is like a EOW symbol. All results are without language model.
['Labels Unit', 'Labels Type', 'Labels #Num', 'WER[%] Hub5’00', 'WER[%] Hub5’00', 'WER[%] Hub5’00', 'WER[%] Hub5’01']
We use RETURNN [zeyer2018:returnn] as the training framework, which builds upon TensorFlow [tensorflow2015]. The advanced decoder is implemented as part of RASR [rybach2011:rasr], while the simple decoder is implemented in pure TensorFlow within RETURNN. All our experiments are performed on the Switchboard 300h English telephone speech corpus [godfrey1992switchboard]. The simple decoder can only produce reasonable results if the label units allow for word disambiguations, such as in the case of graphemes, but also for phonemes with added word-disambiguate symbols. We find that BPE subwords perform much better than single units, both for phonemes and graphemes, and also better than whole words. We also find that a BPE-500 seems to perform best. Note that BPE-500 results in 592 phoneme classes, or 534 grapheme classes.