some effects of duration on vowel recognition this study was designed to examine the role of duration in vowel perception by testing listeners on the identification of cvc syllables generated at different durations test signals consisted of synthesized versions of 300 utterances selected from a large multitalker database of hvd syllables hillenbrand et al j acoust soc am 97 3099 3111 1995 four versions of each utterance were synthesized 1 an original duration set vowel duration matched to the original utterance 2 a neutral duration set duration fixed at 272 ms the grand mean across all vowels 3 a short duration set duration fixed at 144 ms two standard deviations below the mean and 4 a long duration set duration fixed at 400 ms two standard deviations above the mean experiment 1 used a formant synthesizer while a second experiment was an exact replication using a sinusoidal synthesis method that represented the original vowel spectrum more precisely than the formant synthesizer findings included 1 duration

identification of synthetic vowels based on a time varying model of the vocal tract area function the purpose of this study was to conduct an identification experiment with synthetic vowels based on the same sets of speaker dependent area functions as in bunton and story 2009 j acoust soc am 125 19 22 but with additional time varying characteristics that are more representative of natural speech the results indicated that vowels synthesized using an area function model that allows for time variation of the vocal tract shape and includes natural vowel durations were more accurately identified for 7 of 11 english vowels than those based on static area functions

a narrow band pattern matching model of vowel perception the purpose of this paper is to propose and evaluate a new model of vowel perception which assumes that vowel identity is recognized by a template matching process involving the comparison of narrow band input spectra with a set of smoothed spectral shape templates that are learned through ordinary exposure to speech in the present simulation of this process the input spectra are computed over a sufficiently long window to resolve individual harmonics of voiced speech prior to template creation and pattern matching the narrow band spectra are amplitude equalized by a spectrum level normalization process and the information bearing spectral peaks are enhanced by a flooring procedure that zeroes out spectral values below a threshold function consisting of a center weighted running average of spectral amplitudes templates for each vowel category are created simply by averaging the narrow band spectra of like vowels spoken by a panel of talkers in the present implementation separate templates are used for men women and children the pattern matching is implemented with a simple city block distance measure given by the sum of the channel by channel differences between the narrow band input spectrum level equalized and floored and each vowel template spectral movement is taken into account by computing the distance measure at several points throughout the course of the vowel the input spectrum is assigned to the vowel template that results in the smallest difference accumulated over the sequence of spectral slices the model was evaluated using a large database consisting of 12 vowels in hvd context spoken by 45 men 48 women and 46 children the narrow band model classified vowels in this database with a degree of accuracy 91 4 approaching that of human listeners

the use of acoustic cues for phonetic identification effects of spectral degradation and electric hearing although some cochlear implant ci listeners can show good word recognition accuracy it is not clear how they perceive and use the various acoustic cues that contribute to phonetic perceptions in this study the use of acoustic cues was assessed for normal hearing nh listeners in optimal and spectrally degraded conditions and also for ci listeners two experiments tested the tense lax vowel contrast varying in formant structure vowel inherent spectral change and vowel duration and the word final fricative voicing contrast varying in f1 transition vowel duration consonant duration and consonant voicing identification results were modeled using mixed effects logistic regression these experiments suggested that under spectrally degraded conditions nh listeners decrease their use of formant cues and increase their use of durational cues compared to nh listeners ci listeners showed decreased use of spectral cues like formant structure and formant change and consonant voicing and showed greater use of durational cues especially for the fricative contrast the results suggest that although nh and ci listeners may show similar accuracy on basic tests of word phoneme or feature recognition they may be using different perceptual strategies in the process

a discontinuous petrov galerkin method with lagrangian multipliers for second order elliptic problems we present a discontinuous petrov galerkin dpg method for the finite element discretization of second order elliptic boundary value problems the novel approach emanates from a one element weak formulation of the differential problem this procedure which is typical of discontinuous galerkin dg methods is based on introducing variables defined in the interior and on the boundary of the element the interface variables are suitable lagrangian multipliers that enforce interelement continuity of the solution and of its normal derivative thus providing the proper connection between neighboring elements the internal variables can be eliminated in favor of the interface variables using static condensation to end up with a system of reduced size in the sole lagrangian multipliers a stability and convergence analysis of the novel formulation is carried out and its connection with mixed hybrid and dg methods is explored numerical tests on several benchmark problems are included to validate the convergence performance and the flux conservation properties of the dpg method

proto value functions a laplacian framework for learning representation and control in markov decision processes this paper introduces a novel spectral framework for solving markov decision processes mdps by jointly learning representations and optimal policies the major components of the framework described in this paper include i a general scheme for constructing representations or basis functions by diagonalizing symmetric diffusion operators ii a specific instantiation of this approach where global basis functions called proto value functions pvfs are formed using the eigenvectors of the graph laplacian on an undirected graph formed from state transitions induced by the mdp iii a three phased procedure called representation policy iteration comprising of a sample collection phase a representation learning phase that constructs basis functions from samples and a final parameter estimation phase that determines an approximately optimal policy within the linear subspace spanned by the current basis functions iv a specific instantiation of the rpi framework using least squares policy iteration lspi as the parameter estimation method v several strategies for scaling the proposed approach to large discrete and continuous state spaces including the nystrom extension for out of sample interpolation of eigenfunctions and the use of kronecker sum factorization to construct compact eigenfunctions in product spaces such as factored mdps vi finally a series of illustrative discrete and continuous control tasks which both illustrate the concepts and provide a benchmark for evaluating the proposed approach many challenges remain to be addressed in scaling the proposed framework to large mdps and several elaboration of the proposed framework are briefly summarized at the end

de rham diagram for hp finite element spaces abstract we prove that the hp finite elements for h curl spaces introduced in 1 fit into a general de rham diagram involving hp approximations the corresponding interpolation operators generalize the notion of hp interpolation introduced in 2 and are different from the classical operators of nedelec and raviart thomas

a unifying theory of a posteriori finite element error control residual based a posteriori error estimates are derived within a unified setting for lowest order conforming nonconforming and mixed finite element schemes the various residuals are identified for all techniques and problems as the operator norm l of a linear functional of the form in the variable of a sobolev space v the main assumption is that the first order finite element space is included in the kernel ker l of l as a consequence any residual estimator that is a computable bound of l can be used within the proposed frame without further analysis for nonconforming or mixed fe schemes applications are given for the laplace stokes and navier lame equations

a multiscale formulation of the discontinuous petrov galerkin method for advective diffusive problems abstract we consider the discontinuous petrov galerkin method for the advection diffusion model problem and we investigate the application of the variational multiscale method to this formulation we show the exact modeling of the fine scale modes at the element level for the linear case and we discuss the approximate modeling both in the linear and in the non linear cases furthermore we highlight the existing link between this multiscale formulation and the p version of the finite element method numerical examples illustrate the behavior of the proposed scheme

stochastic relaxation gibbs distributions and the bayesian restoration of images we make an analogy between images and statistical mechanics systems pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice like physical system the assignment of an energy function in the physical system determines its gibbs distribution because of the gibbs distribution markov random field mrf equivalence this assignment also determines an mrf image model the energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the mrf for a range of degradation mechanisms including blurring nonlinear deformations and multiplicative or additive noise the posterior distribution is an mrf with a structure akin to the image model by the analogy the posterior distribution defines another imaginary physical system gradual temperature reduction in the physical system isolates low energy states annealing or what is the same thing the most probable states under the gibbs distribution the analogous operation under the posterior distribution yields the maximum a posteriori map estimate of the image given the degraded observations the result is a highly parallel relaxation algorithm for map estimation we establish convergence properties of the algorithm and we experiment with some simple pictures for which good restorations are obtained at low signal to noise ratios

identification of synthetic vowels based on selected vocal tract area functions the purpose of this study was to determine the degree to which synthetic vowel samples based on previously reported vocal tract area functions of eight speakers could be accurately identified by listeners vowels were synthesized with a wave reflection type of vocal tract model coupled to a voice source a particular vowel was generated by specifying an area function that had been derived from previous magnetic resonance imaging based measurements the vowel samples were presented to ten listeners in a forced choice paradigm in which they were asked to identify the vowel results indicated that the vowels i ae and u were identified most accurately for all of speakers the identification errors of the other vowels were typically due to confusions with adjacent vowels

the computational complexity of probabilistic inference using bayesian belief networks research note abstract bayesian belief networks provide a natural efficient method for representing probabilistic dependencies among a set of variables for these reasons numerous researchers are exploring the use of belief networks as a knowledge representation in artificial intelligence algorithms have been developed previously for efficient probabilistic inference using special classes of belief networks more general classes of belief networks however have eluded efforts to develop efficient inference algorithms we show that probabilistic inference using belief networks is np hard therefore it seems unlikely that an exact algorithm can be developed to perform probabilistic inference efficiently over all classes of belief networks this result suggests that research should be directed away from the search for a general efficient probabilistic inference algorithm and toward the design of efficient special case average case and approximation algorithms

vowel identification orthographic perceptual and acoustic aspects this study investigates conditions under which vowels are well recognized and relates perceptual identification of individual tokens to acoustic characteristics results support recent findings that isolated vowels may be readily identified by listeners two experiments provided evidence that certain response tasks result in inflated error rates subsequent experiments showed improved identification in a fixed speaker context compared with randomized speakers for isolated vowels and gated centers performance was worse for gated vowels suggesting that dynamic properties such as duration and diphthongization supplement steady state cues however even speaker randomized gated vowels were well identified 14 errors measures of steady state information formant frequencies and f0 dynamic information formant slopes and duration and speaker information normalization were adopted discriminant analyses of acoustic measurements indicated relatively little overlap between vowel categories using a new technique for relating acoustic measurements of individual tokens with identification by listeners it is shown that a identification performance is clearly related to acoustic characteristics b improvement in the fixed speaker context is correlated with improved statistical separation resulting from formant normalization for the gated vowels and c dynamic information is related to identification differences between full and gated isolated vowels

estimateur d erreur a posteriori hierarchique application aux elements finis mixtes on propose une generalisation de l estimateur a posteriori hierarchique de bank weiser aux formulation mixtes dans le cas d approximations conformes et non conformes avec ou sans integration numerique on presente comme exemples d application la formulation mixte duale du probleme de dirichlet pour le laplacien avec et sans integration numerique

stochastic complexity and generalization error of a restricted boltzmann machine in bayesian estimation in this paper we consider the asymptotic form of the generalization error for the restricted boltzmann machine in bayesian estimation it has been shown that obtaining the maximum pole of zeta functions is related to the asymptotic form of the generalization error for hierarchical learning models watanabe 2001a b the zeta function is defined by using a kullback function we use two methods to obtain the maximum pole a new eigenvalue analysis method and a recursive blowing up process we show that these methods are effective for obtaining the asymptotic form of the generalization error of hierarchical learning models

reinforcement learning with factored states and actions a novel approximation method is presented for approximating the value function and selecting good actions for markov decision processes with large state and action spaces the method approximates state action values as negative free energies in an undirected graphical model called a product of experts the model parameters can be learned efficiently because values and derivatives can be efficiently computed for a product of experts actions can be found even in large factored action spaces by the use of markov chain monte carlo sampling simulation results show that the product of experts approximation can be used to solve large problems in one simulation it is used to find actions in action spaces of size 240

stochastic dynamic programming with factored representations markov decision processes mdps have proven to be popular models for decision theoretic planning but standard dynamic programming algorithms for solving mdps rely on explicit state based specifications and computations to alleviate the combinatorial problems associated with such methods we propose new representational and computational techniques for mdps that exploit certain types of problem structure we use dynamic bayesian networks with decision trees representing the local families of conditional probability distributions to represent stochastic actions in an mdp together with a decision tree representation of rewards based on this representation we develop versions of standard dynamic programming algorithms that directly manipulate decision tree representations of policies and value functions this generally obviates the need for state by state computation aggregating states at the leaves of these trees and requiring computations only for each aggregate state the key to these algorithms is a decision theoretic generalization of classic regression analysis in which we determine the features relevant to predicting expected value we demonstrate the method empirically on several planning problems showing significant savings for certain types of domains we also identify certain classes of problems for which this technique fails to perform well and suggest extensions and related ideas that may prove useful in such circumstances we also briefly describe an approximation scheme based on this approach

acoustic evidence for dynamic formant trajectories in australian english vowels the extent to which it is necessary to model the dynamic behavior of vowel formants to enable vowel separation has been the subject of debate in recent years to investigate this issue a study has been made on the vowels of 132 australian english speakers male and female the degree of vowel separation from the formant values at the target was contrasted to that from modeling the formant contour with discrete cosine transform coefficients the findings are that although it is necessary to model the formant contour to separate out the diphthongs the formant values at the target plus vowel duration are sufficient to separate out the monophthongs however further analysis revealed that there are formant contour differences which benefit the within class separation of the tense lax monophthong pairs

training products of experts by minimizing contrastive divergence it is possible to combine multiple latent variable models of the same data by multiplying their probability distributions together and then renormalizing this way of combining individual expert models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data a product of experts poe is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary training a poe by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule fortunately a poe can be trained using a different objective function called contrastive divergence whose derivatives with regard to the parameters can be approximated accurately and efficiently examples are presented of contrastive divergence learning using several types of expert on several types of data

using speech sounds to test functional spectral resolution in listeners with cochlear implants in this study spectral properties of speech sounds were used to test functional spectral resolution in people who use cochlear implants cis specifically perception of the ba da contrast was tested using two spectral cues formant transitions a fine resolution cue and spectral tilt a coarse resolution cue higher weighting of the formant cues was used as an index of better spectral cue perception participants included 19 ci listeners and 10 listeners with normal hearing nh for whom spectral resolution was explicitly controlled using a noise vocoder with variable carrier filter widths to simulate electrical current spread perceptual weighting of the two cues was modeled with mixed effects logistic regression and was found to systematically vary with spectral resolution the use of formant cues was greatest for nh listeners for unprocessed speech and declined in the two vocoded conditions compared to nh listeners ci listeners relied less on formant transitions and more on spectral tilt cue weighting results showed moderately good correspondence with word recognition scores the current approach to testing functional spectral resolution uses auditory cues that are known to be important for speech categorization and can thus potentially serve as the basis upon which ci processing strategies and innovations are tested

synthesis fidelity and time varying spectral change in vowels recent studies have shown that synthesized versions of american english vowels are less accurately identified when the natural time varying spectral changes are eliminated by holding the formant frequencies constant over the duration of the vowel a limitation of these experiments has been that vowels produced by formant synthesis are generally less accurately identified than the natural vowels after which they are modeled to overcome this limitation a high quality speech analysis synthesis system straight was used to synthesize versions of 12 american english vowels spoken by adults and children vowels synthesized with straight were identified as accurately as the natural versions in contrast with previous results from our laboratory showing identification rates 9 12 lower for the same vowels synthesized using the cascade formant model consistent with earlier studies identification accuracy was not reduced when the fundamental frequency was held constant across the vowel however elimination o

spectral shape features versus formants as acoustic correlates for vowels the first three formants i e the first three spectral prominences of the short time magnitude spectra have been the most commonly used acoustic cues for vowels ever since the work of peterson and barney j acoust soc am 24 175 184 1952 however spectral shape features which encode the global smoothed spectrum provide a more complete spectral description and therefore might be even better acoustic correlates for vowels in this study automatic vowel classification experiments were used to compare formants and spectral shape features for monopthongal vowels spoken in the context of isolated cvc words under a variety of conditions the roles of static and time varying information for vowel discrimination were also compared spectral shape was encoded using the coefficients in a cosine expansion of the nonlinearly scaled magnitude spectrum under almost all conditions investigated in the absence of fundamental frequency f0 information automatic vowel classification based on spectral shape features was superior to that based on formants if f0 was used as an additional feature vowel classification based on spectral shape features was still superior to that based on formants but the differences between the two feature sets were reduced it was also found that the error pattern of perceptual confusions was more closely correlated with errors in automatic classification obtained from spectral shape features than with classification errors from formants therefore it is concluded that spectral shape features are a more complete set of acoustic correlates for vowel identity than are formants in comparing static and time varying features static features were the most important for vowel discrimination but feature trajectories were valuable secondary sources of information

hierarchical mixed hybridized methods for elliptic problems abstract in this article we propose a computational procedure for the efficient implementation of dual mixed hybridized methods of arbitrary degree the procedure relies on the decomposition of the finite element spaces into a vertical p type hierarchy consisting of a lower order part and a defect correction coupled with an additional horizontal decomposition of the defect correction space for the vector variable based on the helmholtz principle an appropriate definition of the basis function set allows us to obtain a systematic substructuring of the block matrix system this property in turn naturally gives rise to an efficient implementation of the procedure through an approximate fixed point block iteration exploiting the equivalence between the principle of defect correction and the variational multiscale modeling theory we also devise and numerically validate a hierarchical a posteriori error estimator for dual mixed methods in hybridized form

convergence analysis of an adaptive nonconforming finite element method an adaptive nonconforming finite element method is developed and analyzed that provides an error reduction due to the refinement process and thus guarantees convergence of the nonconforming finite element approximations the analysis is carried out for the lowest order crouzeix raviart elements and leads to the linear convergence of an appropriate adaptive nonconforming finite element algorithm with respect to the number of refinement levels important tools in the convergence proof are a discrete local efficiency and a quasi orthogonality property the proof does neither require regularity of the solution nor uses duality arguments as a consequence on the data control no particular mesh design has to be monitored