Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Context-Aware Transformer Transducer for Speech Recognition   End-to-end (E2E) automatic speech recognition (ASR) systems often have
difficulty recognizing uncommon words, that appear infrequently in the training
data. One promising method, to improve the recognition accuracy on such rare
words, is to latch onto personalized/contextual information at inference. In
this work, we present a novel context-aware transformer transducer (CATT)
network that improves the state-of-the-art transformer-based ASR system by
taking advantage of such contextual signals. Specifically, we propose a
multi-head attention-based context-biasing network, which is jointly trained
with the rest of the ASR sub-networks. We explore different techniques to
encode contextual data and to create the final attention context vectors. We
also leverage both BLSTM and pretrained BERT based models to encode contextual
data and guide the network training. Using an in-house far-field dataset, we
show that CATT, using a BERT based context encoder, improves the word error
rate of the baseline transformer transducer and outperforms an existing deep
contextual model by 24.2% and 19.4% respectively.

11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
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The categories of this paper are: econ
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th