Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-lingual topic prediction for speech using translations   Given a large amount of unannotated speech in a low-resource language, can we
classify the speech utterances by topic? We consider this question in the
setting where a small amount of speech in the low-resource language is paired
with text translations in a high-resource language. We develop an effective
cross-lingual topic classifier by training on just 20 hours of translated
speech, using a recent model for direct speech-to-text translation. While the
translations are poor, they are still good enough to correctly classify the
topic of 1-minute speech segments over 70% of the time - a 20% improvement over
a majority-class baseline. Such a system could be useful for humanitarian
applications like crisis response, where incoming speech in a foreign
low-resource language must be quickly assessed for further action.

31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat