Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
Abstract
Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexicon and alignments. However, this poses a new problem of requiring more data compared to conventional DNN-HMM systems. In this work, we attempt to use data from 10 BABEL languages to build a multi-lingual seq2seq model as a prior model, and then port them towards 4 other BABEL languages using transfer learning approach. We also explore different architectures for improving the prior multilingual seq2seq model. The paper also discusses the effect of integrating a recurrent neural network language model (RNNLM) with a seq2seq model during decoding. Experimental results show that the transfer learning approach from the multilingual model shows substantial gains over monolingual models across all 4 BABEL languages. Incorporating an RNNLM also brings significant improvements in terms of %WER, and achieves recognition performance comparable to the models trained with twice more training data.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2018
- DOI:
- arXiv:
- arXiv:1810.03459
- Bibcode:
- 2018arXiv181003459C
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Machine Learning;
- Computer Science - Sound;
- Electrical Engineering and Systems Science - Audio and Speech Processing