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» Modelling Smooth Paths Using Gaussian Processes
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ICASSP
2010
IEEE
13 years 11 months ago
Statistical approach to enhancing esophageal speech based on Gaussian mixture models
This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectome...
Hironori Doi, Keigo Nakamura, Tomoki Toda, Hiroshi...
UAI
2008
14 years 12 days ago
Learning Hidden Markov Models for Regression using Path Aggregation
We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for re...
Keith Noto, Mark Craven
ACMICEC
2007
ACM
107views ECommerce» more  ACMICEC 2007»
14 years 3 months ago
A family of growth models for representing the price process in online auctions
Bids during an online auction arrive at unequally-spaced discrete time points. Our goal is to capture the entire continuous price-evolution function by representing it as a functi...
Valerie Hyde, Wolfgang Jank, Galit Shmueli
ICASSP
2011
IEEE
13 years 2 months ago
On the instantaneous frequency smoothing for signals with quasi-linear frequency changes
The problem of estimation of the slowly-varying instantaneous frequency of a nonstationary complex sinusoidal signal buried in noise is considered. This problem is usually solved ...
Maciej Niedzwiecki, Michal Stanislaw Meller
CVPR
2007
IEEE
15 years 1 months ago
Learning Gaussian Conditional Random Fields for Low-Level Vision
Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented u...
Marshall F. Tappen, Ce Liu, Edward H. Adelson, Wil...