Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spoken Term Detection Methods for Sparse Transcription in Very
  Low-resource Settings   We investigate the efficiency of two very different spoken term detection
approaches for transcription when the available data is insufficient to train a
robust ASR system. This work is grounded in very low-resource language
documentation scenario where only few minutes of recording have been
transcribed for a given language so far.Experiments on two oral languages show
that a pretrained universal phone recognizer, fine-tuned with only a few
minutes of target language speech, can be used for spoken term detection with a
better overall performance than a dynamic time warping approach. In addition,
we show that representing phoneme recognition ambiguity in a graph structure
can further boost the recall while maintaining high precision in the low
resource spoken term detection task.

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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat