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Attribute based shared hidden layers for cross-language knowledge transfer

Abstract:

Deep neural network (DNN) acoustic models can be adapted to under-resourced languages by transferring the hidden layers. An analogous transfer problem is popular as few-shot learning to recognise scantily seen objects based on their meaningful attributes. In similar way, this paper proposes a principled way to represent the hidden layers of DNN in terms of attributes shared across languages. The diverse phoneme sets of different languages can be represented in terms of phonological features t...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1109/SLT.2016.7846327

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Department:
Oxford, HUM, Ling Philology & Phonetics Fac
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Department:
Oxford, HUM, Ling Philology & Phonetics Fac
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Grant:
Proof of Concept FLEX-SR award no. 632226
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2017-02-09
Acceptance date:
2016-09-16
DOI:
Pubs id:
pubs:664070
URN:
uri:a6db756d-e151-4cd6-804a-9c95b5f425cd
UUID:
uuid:a6db756d-e151-4cd6-804a-9c95b5f425cd
Local pid:
pubs:664070

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