109,904 research outputs found
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
Efficient Decomposed Learning for Structured Prediction
Structured prediction is the cornerstone of several machine learning
applications. Unfortunately, in structured prediction settings with expressive
inter-variable interactions, exact inference-based learning algorithms, e.g.
Structural SVM, are often intractable. We present a new way, Decomposed
Learning (DecL), which performs efficient learning by restricting the inference
step to a limited part of the structured spaces. We provide characterizations
based on the structure, target parameters, and gold labels, under which DecL is
equivalent to exact learning. We then show that in real world settings, where
our theoretical assumptions may not completely hold, DecL-based algorithms are
significantly more efficient and as accurate as exact learning.Comment: ICML201
On Correcting Inputs: Inverse Optimization for Online Structured Prediction
Algorithm designers typically assume that the input data is correct, and then
proceed to find "optimal" or "sub-optimal" solutions using this input data.
However this assumption of correct data does not always hold in practice,
especially in the context of online learning systems where the objective is to
learn appropriate feature weights given some training samples. Such scenarios
necessitate the study of inverse optimization problems where one is given an
input instance as well as a desired output and the task is to adjust the input
data so that the given output is indeed optimal. Motivated by learning
structured prediction models, in this paper we consider inverse optimization
with a margin, i.e., we require the given output to be better than all other
feasible outputs by a desired margin. We consider such inverse optimization
problems for maximum weight matroid basis, matroid intersection, perfect
matchings, minimum cost maximum flows, and shortest paths and derive the first
known results for such problems with a non-zero margin. The effectiveness of
these algorithmic approaches to online learning for structured prediction is
also discussed.Comment: Conference version to appear in FSTTCS, 201
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