Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F19%3APU136135" target="_blank" >RIV/00216305:26230/19:PU136135 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8683380" target="_blank" >https://ieeexplore.ieee.org/document/8683380</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP.2019.8683380" target="_blank" >10.1109/ICASSP.2019.8683380</a>
Alternative languages
Result language
angličtina
Original language name
Language Model Integration Based on Memory Control for Sequence to Sequence Speech Recognition
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
ISBN
978-1-5386-4658-8
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
6191-6195
Publisher name
IEEE Signal Processing Society
Place of publication
Brighton
Event location
Brighton
Event date
May 12, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000482554006084