Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Shallow Fusion for RNN-T Personalization   End-to-end models in general, and Recurrent Neural Network Transducer (RNN-T)
in particular, have gained significant traction in the automatic speech
recognition community in the last few years due to their simplicity,
compactness, and excellent performance on generic transcription tasks. However,
these models are more challenging to personalize compared to traditional hybrid
systems due to the lack of external language models and difficulties in
recognizing rare long-tail words, specifically entity names. In this work, we
present novel techniques to improve RNN-T's ability to model rare WordPieces,
infuse extra information into the encoder, enable the use of alternative
graphemic pronunciations, and perform deep fusion with personalized language
models for more robust biasing. We show that these combined techniques result
in 15.4%-34.5% relative Word Error Rate improvement compared to a strong RNN-T
baseline which uses shallow fusion and text-to-speech augmentation. Our work
helps push the boundary of RNN-T personalization and close the gap with hybrid
systems on use cases where biasing and entity recognition are crucial.

18
The categories of this paper are: econ
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph