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We consider how to optimize the acoustic features used by hidden Markov models (HMMs) for automatic speech recognition (ASR). We investigate a mistake-driven algorithm that discriminatively reweights the acoustic features in order to separate the log-likelihoods of correct and incorrect transcriptions by a large margin. The algorithm simultaneously optimizes the HMM parameters in the back end by adapting...
We develop a framework to detect when certain sounds are present in a mixed audio signal. We focus on the regime where out of a large number of possible sounds, a small but unknown number are combined and overlapped to yield the observed signal. To infer which sounds are present, we attempt to decompose the observed signal as a linear combination of a small number of sources. To encourage sparse solutions...
In this paper we describe an interdisciplinary collaboration between researchers in machine learning and oceanography. The collaboration was formed to study the problem of open ocean biome classification. Biomes are regions on Earth with similar climate (e.g., temperature and rainfall) and vegetation structure (e.g., grasslands, coniferous forests, and deserts). To discover biomes in the open ocean,...
Sight-reading is the ability to read and perform music from a written score with little or no preparation. Though an integral part of musicianship, it is rarely or minimally addressed in traditional music lessons. In this paper, we describe a real-time system for sight-reading evaluation of solo instrumental music. The system is trained to recognize monophonic and polyphonic music from acoustic instruments...
We develop a framework for large margin classification by Gaussian mixture models (GMMs). Large margin GMMs have many parallels to support vector machines (SVMs) but use ellipsoids to model classes instead of half-spaces. Model parameters are trained discriminatively to maximize the margin of correct classification, as measured in terms of Mahalanobis distances. The required optimization is convex...
The responsiveness of networked applications is limited by communications delays, making network distance an important parameter in optimizing the choice of communications peers. Since accurate global snapshots are difficult and expensive to gather and maintain, it is desirable to use sampling techniques in the Internet to predict unknown network distances from a set of partially observed measurements...
Summary form only given. In many applications of machine learning, it is cheap to collect unlabeled examples but expensive to collect labeled ones. Often, the unlabeled examples can provide useful auxiliary information by revealing that the data has a simple underlying structure (M. Belkin and P. Niyogi, 2004). Of particular interest is the case when seemingly high dimensional data can be described...
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