RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
Table 5: Performance comparison on Switchboard, trained on 300h. hybrid1 is the IBM 2017 ResNet model Saon et al. (2017). hybrid2 trained with Lattice-free MMI Hadian et al. (2018). CTC3 is the Baidu 2014 DeepSpeech model Hannun et al. (2014). Our attention model does not use any language model.
['model', 'training', 'WER [%] Hub5’00', 'WER [%] Hub5’00', 'WER [%] Hub5’00', 'WER [%] Hub5’01']
We also have preliminary results with recurrent attention models for speech recognition on the Switchboard task, which we trained on the 300h trainset. We report on both the Switchboard (SWB) and the CallHome (CH) part of Hub5’00 and Hub5’01. We also compare to a conventional frame-wise trained hybrid deep bidirectional LSTM with 6 layers Zeyer et al. The frame-wise trained hybrid model also uses focal loss Lin et al. All the hybrid models use a phonetic lexicon and an external 4-gram language model which was trained on the transcripts of both the Switchboard and the Fisher corpus. The attention model does not use any external language model nor a phonetic lexicon. Its output labels are byte-pair encoded subword units Sennrich et al. It has a 6 layer bidirectional encoder, which also applies max-pooling in the time dimension , i.e. it reduces the input sequence by factor 8. To our knowledge, this is the best reported result for an end-to-end system on Switchboard 300h without using a language model or the lexicon. For comparison, we also selected comparable results from the literature. From these, the Baidu DeepSpeech CTC model is modeled on characters and does not use the lexicon but it does use a language model.