Publication Details

Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition

CHO, J.; WATANABE, S.; HORI, T.; BASKAR, M.; INAGUMA, H.; VILLALBA LOPEZ, J.; DEHAK, N. Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition. In Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP). Brighton: IEEE Signal Processing Society, 2019. p. 6191-6195. ISBN: 978-1-5386-4658-8.
Czech title
Integrace jazykového modelu v sequence-to-sequence rozpoznávání řeči založená na řízení paměťových buněk
Type
conference paper
Language
English
Authors
CHO, J.
Watanabe Shinji (FIT)
HORI, T.
Baskar Murali Karthick, Ing., Ph.D.
INAGUMA, H.
VILLALBA LOPEZ, J.
Dehak Najim
URL
Keywords

Automatic speech recognition (ASR), sequence to sequence, language model, shallow fusion, deep fusion, cold fusion

Abstract

In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained language model (LM). Our proposed fusion methods focus on the memory cell state and the hidden state in the seq2seq decoder long short-term memory (LSTM), and the memory cell state is updated by the LM unlike the prior studies. This means the memory retained by the main seq2seq would be adjusted by the external LM. These fusion methods have several variants depending on the architecture of this memory cell update and the use of memory cell and hidden states which directly affects the final label inference. We performed the experiments to show the effectiveness of the proposed methods in a mono-lingual ASR setup on the Librispeech corpus and in a transfer learning setup from a multilingual ASR (MLASR) base model to a low-resourced language. In Librispeech, our best model improved WER by 3.7%, 2.4% for test clean, test other relatively to the shallow fusion baseline, with multilevel decoding. In transfer learning from an MLASR base model to the IARPA Babel Swahili model, the best scheme improved the transferred model on eval set by 9.9%, 9.8% in CER, WER relatively to the 2-stage transfer baseline.

Published
2019
Pages
6191–6195
Proceedings
Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
ISBN
978-1-5386-4658-8
Publisher
IEEE Signal Processing Society
Place
Brighton
DOI
10.1109/ICASSP.2019.8683380
UT WoS
000482554006084
EID Scopus
2-s2.0-85069000387
BibTeX 
@inproceedings{BUT163488,
  author="CHO, J. and WATANABE, S. and HORI, T. and BASKAR, M. and INAGUMA, H. and VILLALBA LOPEZ, J. and DEHAK, N.",
  title="Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition",
  booktitle="Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)",
  year="2019",
  pages="6191--6195",
  publisher="IEEE Signal Processing Society",
  address="Brighton",
  doi="10.1109/ICASSP.2019.8683380",
  isbn="978-1-5386-4658-8",
  url="https://ieeexplore.ieee.org/document/8683380"
}
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