427 research outputs found
A sticky HDP-HMM with application to speaker diarization
We consider the problem of speaker diarization, the problem of segmenting an
audio recording of a meeting into temporal segments corresponding to individual
speakers. The problem is rendered particularly difficult by the fact that we
are not allowed to assume knowledge of the number of people participating in
the meeting. To address this problem, we take a Bayesian nonparametric approach
to speaker diarization that builds on the hierarchical Dirichlet process hidden
Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006)
1566--1581]. Although the basic HDP-HMM tends to over-segment the audio
data---creating redundant states and rapidly switching among them---we describe
an augmented HDP-HMM that provides effective control over the switching rate.
We also show that this augmentation makes it possible to treat emission
distributions nonparametrically. To scale the resulting architecture to
realistic diarization problems, we develop a sampling algorithm that employs a
truncated approximation of the Dirichlet process to jointly resample the full
state sequence, greatly improving mixing rates. Working with a benchmark NIST
data set, we show that our Bayesian nonparametric architecture yields
state-of-the-art speaker diarization results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS395 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Centered Partition Process: Informative Priors for Clustering
There is a very rich literature proposing Bayesian approaches for clustering
starting with a prior probability distribution on partitions. Most approaches
assume exchangeability, leading to simple representations in terms of
Exchangeable Partition Probability Functions (EPPF). Gibbs-type priors
encompass a broad class of such cases, including Dirichlet and Pitman-Yor
processes. Even though there have been some proposals to relax the
exchangeability assumption, allowing covariate-dependence and partial
exchangeability, limited consideration has been given on how to include
concrete prior knowledge on the partition. For example, we are motivated by an
epidemiological application, in which we wish to cluster birth defects into
groups and we have prior knowledge of an initial clustering provided by
experts. As a general approach for including such prior knowledge, we propose a
Centered Partition (CP) process that modifies the EPPF to favor partitions
close to an initial one. Some properties of the CP prior are described, a
general algorithm for posterior computation is developed, and we illustrate the
methodology through simulation examples and an application to the motivating
epidemiology study of birth defects
A nonparametric HMM for genetic imputation and coalescent inference
Genetic sequence data are well described by hidden Markov models (HMMs) in
which latent states correspond to clusters of similar mutation patterns. Theory
from statistical genetics suggests that these HMMs are nonhomogeneous (their
transition probabilities vary along the chromosome) and have large support for
self transitions. We develop a new nonparametric model of genetic sequence
data, based on the hierarchical Dirichlet process, which supports these self
transitions and nonhomogeneity. Our model provides a parameterization of the
genetic process that is more parsimonious than other more general nonparametric
models which have previously been applied to population genetics. We provide
truncation-free MCMC inference for our model using a new auxiliary sampling
scheme for Bayesian nonparametric HMMs. In a series of experiments on male X
chromosome data from the Thousand Genomes Project and also on data simulated
from a population bottleneck we show the benefits of our model over the popular
finite model fastPHASE, which can itself be seen as a parametric truncation of
our model. We find that the number of HMM states found by our model is
correlated with the time to the most recent common ancestor in population
bottlenecks. This work demonstrates the flexibility of Bayesian nonparametrics
applied to large and complex genetic data
Bayesian Nonparametric Hidden Semi-Markov Models
There is much interest in the Hierarchical Dirichlet Process Hidden Markov
Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous
Hidden Markov Model for learning from sequential and time-series data. However,
in many settings the HDP-HMM's strict Markovian constraints are undesirable,
particularly if we wish to learn or encode non-geometric state durations. We
can extend the HDP-HMM to capture such structure by drawing upon
explicit-duration semi-Markovianity, which has been developed mainly in the
parametric frequentist setting, to allow construction of highly interpretable
models that admit natural prior information on state durations.
In this paper we introduce the explicit-duration Hierarchical Dirichlet
Process Hidden semi-Markov Model (HDP-HSMM) and develop sampling algorithms for
efficient posterior inference. The methods we introduce also provide new
methods for sampling inference in the finite Bayesian HSMM. Our modular Gibbs
sampling methods can be embedded in samplers for larger hierarchical Bayesian
models, adding semi-Markov chain modeling as another tool in the Bayesian
inference toolbox. We demonstrate the utility of the HDP-HSMM and our inference
methods on both synthetic and real experiments
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