Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition   Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here
we propose an enhanced ASR-TTS (EAT) model that incorporates two main features:
1) The ASR$\rightarrow$TTS direction is equipped with a language model reward
to penalize the ASR hypotheses before forwarding it to TTS. 2) In the
TTS$\rightarrow$ASR direction, a hyper-parameter is introduced to scale the
attention context from synthesized speech before sending it to ASR to handle
out-of-domain data. Training strategies and the effectiveness of the EAT model
are explored under out-of-domain data conditions. The results show that EAT
reduces the performance gap between supervised and self-supervised training
significantly by absolute 2.6\% and 2.7\% on Librispeech and BABEL
respectively.

17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th