The design of a new methodology for representing the relationship between two sets of spectral envelopes and the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods.
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory, and builds on recent developments to present a self-contained view.
It is proved that for arbitrary bounded rewards, the KL-UCB algorithm satisfies a uniformly better regret bound than UCB or UCB2; second, in the special case of Bernoulli rewards, it reaches the lower bound of Lai and Robbins.
This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.
This work introduces generic notions of complexity for the two dominant frameworks considered in the literature: fixed-budget and fixed-confidence settings, and provides the first known distribution-dependent lower bound on the complexity that involves information-theoretic quantities and holds when m ≥ 1 under general assumptions.
The analysis highlights a key difficulty in generalizing linear bandit algorithms to the non-linear case, which is solved in GLM-UCB by focusing on the reward space rather than on the parameter space, and provides a tuning method based on asymptotic arguments, which leads to significantly better practical performance.
A generic on‐line version of the expectation–maximization (EM) algorithm applicable to latent variable models of independent observations that is suitable for conditional models, as illustrated in the case of the mixture of linear regressions model.
It is proved that the corresponding algorithm, termed BayesUCB, satisfies finite-time regret bounds that imply its asymptotic optimality and gives a general formulation for a class of Bayesian index policies that rely on quantiles of the posterior distribution.
It is first shown using simple arguments that the so-called residual and stratified methods do yield an improvement over the basic multinomial resampling approach, and a central limit theorem is established for the case where resamplings is performed using the residual approach.
A study of the noise suppression technique proposed by Ephraim and Malah (1984,1985) and it is demonstrated how this artifact is actually eliminated without bringing distortion to the recorded signal even if the noise is only poorly stationary.