121 research outputs found
On-line adaptation of the SCHMM parameters based on the segmental quasi-bayes learning for speech recognition
On-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary.published_or_final_versio
Large portfolio losses: A dynamic contagion model
Using particle system methodologies we study the propagation of financial
distress in a network of firms facing credit risk. We investigate the
phenomenon of a credit crisis and quantify the losses that a bank may suffer in
a large credit portfolio. Applying a large deviation principle we compute the
limiting distributions of the system and determine the time evolution of the
credit quality indicators of the firms, deriving moreover the dynamics of a
global financial health indicator. We finally describe a suitable version of
the "Central Limit Theorem" useful to study large portfolio losses. Simulation
results are provided as well as applications to portfolio loss distribution
analysis.Comment: Published in at http://dx.doi.org/10.1214/08-AAP544 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition
We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.published_or_final_versio
Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition
A theoretical framework for Bayesian adaptive training of the parameters of a discrete hidden Markov model (DHMM) and of a semi-continuous HMM (SCHMM) with Gaussian mixture state observation densities is presented. In addition to formulating the forward-backward MAP (maximum a posteriori) and the segmental MAP algorithms for estimating the above HMM parameters, a computationally efficient segmental quasi-Bayes algorithm for estimating the state-specific mixture coefficients in SCHMM is developed. For estimating the parameters of the prior densities, a new empirical Bayes method based on the moment estimates is also proposed. The MAP algorithms and the prior parameter specification are directly applicable to training speaker adaptive HMMs. Practical issues related to the use of the proposed techniques for HMM-based speaker adaptation are studied. The proposed MAP algorithms are shown to be effective especially in the cases in which the training or adaptation data are limited.published_or_final_versio
A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal
Unveiling meaningful geophysical information from seismic data requires to
deal with both random and structured "noises". As their amplitude may be
greater than signals of interest (primaries), additional prior information is
especially important in performing efficient signal separation. We address here
the problem of multiple reflections, caused by wave-field bouncing between
layers. Since only approximate models of these phenomena are available, we
propose a flexible framework for time-varying adaptive filtering of seismic
signals, using sparse representations, based on inaccurate templates. We recast
the joint estimation of adaptive filters and primaries in a new convex
variational formulation. This approach allows us to incorporate plausible
knowledge about noise statistics, data sparsity and slow filter variation in
parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a
constrained minimization problem that alleviates standard regularization issues
in finding hyperparameters. The approach demonstrates significantly good
performance in low signal-to-noise ratio conditions, both for simulated and
real field seismic data
Multilingual audio information management system based on semantic knowledge in complex environments
This paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users.This work is being funded by Grants: TEC201677791-C4 from Plan Nacional de I + D + i, Ministry of Economic Affairs and Competitiveness of Spain and from the DomusVi Foundation Kms para recorder, the Basque Government (ELKARTEK KK-2018/00114, GEJ IT1189-19, the Government of Gipuzkoa (DG18/14 DG17/16), UPV/EHU (GIU19/090), COST ACTION (CA18106, CA15225)
On adaptive decision rules and decision parameter adaptation for automatic speech recognition
Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio
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Multilingual audio information management system based on semantic knowledge in complex environments
AbstractThis paper proposes a multilingual audio information management system based on semantic knowledge in complex environments. The complex environment is defined by the limited resources (financial, material, human, and audio resources); the poor quality of the audio signal taken from an internet radio channel; the multilingual context (Spanish, French, and Basque that is in under-resourced situation in some areas); and the regular appearance of cross-lingual elements between the three languages. In addition to this, the system is also constrained by the requirements of the local multilingual industrial sector. We present the first evolutionary system based on a scalable architecture that is able to fulfill these specifications with automatic adaptation based on automatic semantic speech recognition, folksonomies, automatic configuration selection, machine learning, neural computing methodologies, and collaborative networks. As a result, it can be said that the initial goals have been accomplished and the usability of the final application has been tested successfully, even with non-experienced users.</jats:p
MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification
Next-generation RNA sequencing (RNA-seq) technology has been widely used to
assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq
data offer insight into gene expression levels and transcriptome structures,
enabling us to better understand the regulation of gene expression and
fundamental biological processes. Accurate isoform quantification from RNA-seq
data is challenging due to the information loss in sequencing experiments. A
recent accumulation of multiple RNA-seq data sets from the same tissue or cell
type provides new opportunities to improve the accuracy of isoform
quantification. However, existing statistical or computational methods for
multiple RNA-seq samples either pool the samples into one sample or assign
equal weights to the samples when estimating isoform abundance. These methods
ignore the possible heterogeneity in the quality of different samples and could
result in biased and unrobust estimates. In this article, we develop a method,
which we call "joint modeling of multiple RNA-seq samples for accurate isoform
quantification" (MSIQ), for more accurate and robust isoform quantification by
integrating multiple RNA-seq samples under a Bayesian framework. Our method
aims to (1) identify a consistent group of samples with homogeneous quality and
(2) improve isoform quantification accuracy by jointly modeling multiple
RNA-seq samples by allowing for higher weights on the consistent group. We show
that MSIQ provides a consistent estimator of isoform abundance, and we
demonstrate the accuracy and effectiveness of MSIQ compared with alternative
methods through simulation studies on D. melanogaster genes. We justify MSIQ's
advantages over existing approaches via application studies on real RNA-seq
data from human embryonic stem cells, brain tissues, and the HepG2 immortalized
cell line
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