Multilingual Sequence-to-sequence Speech Recognition: Architecture, Transfer Learning, And Language Modeling | Awesome LLM Papers Add your paper to Awesome LLM Papers

Multilingual Sequence-to-sequence Speech Recognition: Architecture, Transfer Learning, And Language Modeling

Jaejin Cho, Murali Karthick Baskar, Ruizhi Li, Matthew Wiesner, Sri Harish Mallidi, Nelson Yalta, Martin Karafiat, Shinji Watanabe, Takaaki Hori . 2018 IEEE Spoken Language Technology Workshop (SLT) 2018 – 67 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Compositional Generalization Fine Tuning Interdisciplinary Approaches Model Architecture SLT Training Techniques

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.

Similar Work