We study the segmental recurrent neural network for end-to-end acoustic modelling. This model connects the segmental conditional random field (CRF) with a recurrent neural network (RNN) used for feature extraction. Compared to most previous CRF-based acoustic models, it does not rely on an external system to provide features or segmentation boundaries. Instead, this model marginalises out all the possible segmentations, and features are extracted from the RNN trained together with the segmental CRF. In essence, this model is self-contained and can be trained end-to-end. In this paper, we discuss practical training and decoding issues as well as the method to speed up the training in the context of speech recognition. We performed experiments on the TIMIT dataset. We achieved 17.3 phone error rate (PER) from the first-pass decoding --- the best reported result using CRFs, despite the fact that we only used a zeroth-order CRF and without using any language model.
In Table , we compare our result to other reported results using segmental CRFs as well as recent end-to-end systems. Previous state-of-the-art result using segmental CRFs on the TIMIT dataset is reported in @cite_12 , where the first-pass decoding was used to prune the search space, and the second-pass was used to re-score the hypothesis using various features including neural network features. Besides, the ground-truth segmentation was used in @cite_12 . We achieved considerably lower PER with first-pass decoding, despite the fact that our CRF was zeroth-order, and we did not use any language model. Furthermore, our results are also comparable to that from the CTC and attention-based RNN end-to-end systems. The accuracy of segmental RNNs may be further improved by using higher-order CRFs or incorporating a language model into the decode step, and using beam search to reduce the search error.
@cite_12
2291022022
We study the segmental recurrent neural network for end-to-end acoustic modelling. This model connects the segmental conditional random field (CRF) with a recurrent neural network (RNN) used for feature extraction. Compared to most previous CRF-based acoustic models, it does not rely on an external system to provide features or segmentation boundaries. Instead, this model marginalises out all the possible segmentations, and features are extracted from the RNN trained together with the segmental CRF. In essence, this model is self-contained and can be trained end-to-end. In this paper, we discuss practical training and decoding issues as well as the method to speed up the training in the context of speech recognition. We performed experiments on the TIMIT dataset. We achieved 17.3 phone error rate (PER) from the first-pass decoding --- the best reported result using CRFs, despite the fact that we only used a zeroth-order CRF and without using any language model.