Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Measurement of Pre-aspiration   Pre-aspiration is defined as the period of glottal friction occurring in
sequences of vocalic/consonantal sonorants and phonetically voiceless
obstruents. We propose two machine learning methods for automatic measurement
of pre-aspiration duration: a feedforward neural network, which works at the
frame level; and a structured prediction model, which relies on manually
designed feature functions, and works at the segment level. The input for both
algorithms is a speech signal of an arbitrary length containing a single
obstruent, and the output is a pair of times which constitutes the
pre-aspiration boundaries. We train both models on a set of manually annotated
examples. Results suggest that the structured model is superior to the
frame-based model as it yields higher accuracy in predicting the boundaries and
generalizes to new speakers and new languages. Finally, we demonstrate the
applicability of our structured prediction algorithm by replicating linguistic
analysis of pre-aspiration in Aberystwyth English with high correlation.

30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci