On-the-Fly Text Retrieval for end-to-end ASR Adaptation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149423" target="_blank" >RIV/00216305:26230/23:PU149423 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095857" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095857</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP49357.2023.10095857" target="_blank" >10.1109/ICASSP49357.2023.10095857</a>
Alternative languages
Result language
angličtina
Original language name
On-the-Fly Text Retrieval for end-to-end ASR Adaptation
Original language description
End-to-end speech recognition models are improved by incorporat- ing external text sources, typically by fusion with an external lan- guage model. Such language models have to be retrained whenever the corpus of interest changes. Furthermore, since they store the entire corpus in their parameters, rare words can be challenging to recall. In this work, we propose augmenting a transducer-based ASR model with a retrieval language model, which directly retrieves from an external text corpus plausible completions for a partial ASR hy- pothesis. These completions are then integrated into subsequent pre- dictions by an adapter, which is trained once, so that the corpus of interest can be switched without incurring the computational over- head of retraining. Our experiments show that the proposed model significantly improves the performance of a transducer baseline on a pair of question-answering datasets. Further, it outperforms shallow fusion on recognition of named entities by about 7% relative; when the two are combined, the relative improvement increases to 13%
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
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 ICASSP 2023
ISBN
978-1-7281-6327-7
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
IEEE Signal Processing Society
Place of publication
Rhodes Island
Event location
Rhodes Island, Greece
Event date
Jun 4, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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