Detail výsledku
On-the-Fly Text Retrieval for end-to-end ASR Adaptation
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%
retrieval, language model, domain adaptation, end-to-end ASR, RNN transducer, contextual biasing
@inproceedings{BUT185196,
author="YUSUF, B. and GOURAV, A. and GANDHE, A. and BULYKO, I.",
title="On-the-Fly Text Retrieval for end-to-end ASR Adaptation",
booktitle="Proceedings of ICASSP 2023",
year="2023",
pages="1--5",
publisher="IEEE Signal Processing Society",
address="Rhodes Island",
doi="10.1109/ICASSP49357.2023.10095857",
isbn="978-1-7281-6327-7",
url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095857"
}