Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Phoneme Boundary Detection using Learnable Segmental Features   Phoneme boundary detection plays an essential first step for a variety of
speech processing applications such as speaker diarization, speech science,
keyword spotting, etc. In this work, we propose a neural architecture coupled
with a parameterized structured loss function to learn segmental
representations for the task of phoneme boundary detection. First, we evaluated
our model when the spoken phonemes were not given as input. Results on the
TIMIT and Buckeye corpora suggest that the proposed model is superior to the
baseline models and reaches state-of-the-art performance in terms of F1 and
R-value. We further explore the use of phonetic transcription as additional
supervision and show this yields minor improvements in performance but
substantially better convergence rates. We additionally evaluate the model on a
Hebrew corpus and demonstrate such phonetic supervision can be beneficial in a
multi-lingual setting.

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The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
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The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
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The categories of this paper are: astro-ph