2,554 research outputs found
Who Spoke What? A Latent Variable Framework for the Joint Decoding of Multiple Speakers and their Keywords
In this paper, we present a latent variable (LV) framework to identify all
the speakers and their keywords given a multi-speaker mixture signal. We
introduce two separate LVs to denote active speakers and the keywords uttered.
The dependency of a spoken keyword on the speaker is modeled through a
conditional probability mass function. The distribution of the mixture signal
is expressed in terms of the LV mass functions and speaker-specific-keyword
models. The proposed framework admits stochastic models, representing the
probability density function of the observation vectors given that a particular
speaker uttered a specific keyword, as speaker-specific-keyword models. The LV
mass functions are estimated in a Maximum Likelihood framework using the
Expectation Maximization (EM) algorithm. The active speakers and their keywords
are detected as modes of the joint distribution of the two LVs. In mixture
signals, containing two speakers uttering the keywords simultaneously, the
proposed framework achieves an accuracy of 82% for detecting both the speakers
and their respective keywords, using Student's-t mixture models as
speaker-specific-keyword models.Comment: 6 pages, 2 figures Submitted to : IEEE Signal Processing Letter
Budget Constrained Auctions with Heterogeneous Items
In this paper, we present the first approximation algorithms for the problem
of designing revenue optimal Bayesian incentive compatible auctions when there
are multiple (heterogeneous) items and when bidders can have arbitrary demand
and budget constraints. Our mechanisms are surprisingly simple: We show that a
sequential all-pay mechanism is a 4 approximation to the revenue of the optimal
ex-interim truthful mechanism with discrete correlated type space for each
bidder. We also show that a sequential posted price mechanism is a O(1)
approximation to the revenue of the optimal ex-post truthful mechanism when the
type space of each bidder is a product distribution that satisfies the standard
hazard rate condition. We further show a logarithmic approximation when the
hazard rate condition is removed, and complete the picture by showing that
achieving a sub-logarithmic approximation, even for regular distributions and
one bidder, requires pricing bundles of items. Our results are based on
formulating novel LP relaxations for these problems, and developing generic
rounding schemes from first principles. We believe this approach will be useful
in other Bayesian mechanism design contexts.Comment: Final version accepted to STOC '10. Incorporates significant reviewer
comment
- …