Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GIPFA: Generating IPA Pronunciation from Audio   Transcribing spoken audio samples into the International Phonetic Alphabet
(IPA) has long been reserved for experts. In this study, we examine the use of
an Artificial Neural Network (ANN) model to automatically extract the IPA
phonemic pronunciation of a word based on its audio pronunciation, hence its
name Generating IPA Pronunciation From Audio (GIPFA). Based on the French
Wikimedia dictionary, we trained our model which then correctly predicted 75%
of the IPA pronunciations tested. Interestingly, by studying inference errors,
the model made it possible to highlight possible errors in the dataset as well
as to identify the closest phonemes in French.

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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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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: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: quant-ph