Publication Details
Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition
Watanabe Shinji
HORI, T.
Baskar Murali Karthick, Ing., Ph.D.
INAGUMA, H.
VILLALBA LOPEZ, J.
Dehak Najim
Automatic speech recognition (ASR), sequence to sequence, language model, shallow
fusion, deep fusion, cold fusion
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.
@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"
}