@inproceedings{11917, abstract = {{In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm.}}, author = {{Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}}, booktitle = {{38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}}, issn = {{1520-6149}}, keywords = {{correlation methods, estimation theory, hidden Markov models, iterative methods, probability, spectral analysis, speech processing, 2D HMM, SPP estimates, iterative algorithm, posterior probability estimation, spectral correlation, speech presence probability estimation, state-of-the-art SPP estimation algorithm, temporal correlation, turbo principle, two-dimensional hidden Markov model, Correlation, Decoding, Estimation, Iterative decoding, Noise, Speech, Vectors}}, pages = {{863--867}}, title = {{{Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation}}}, doi = {{10.1109/ICASSP.2013.6637771}}, year = {{2013}}, } @article{34886, abstract = {{We give asymptotic upper and lower bounds for the number of squarefree d (0 < d ≤ X) such that the equation x² − dy²= −1 is solvable. These estimates, as usual, can equivalently be interpreted in terms of real quadratic fields with a fundamental unit with norm −1 and give strong evidence in the direction of a conjecture due to P. Stevenhagen.}}, author = {{Fouvry, Étienne and Klüners, Jürgen}}, issn = {{0003-486X}}, journal = {{Annals of Mathematics}}, keywords = {{Statistics, Probability and Uncertainty, Mathematics (miscellaneous)}}, number = {{3}}, pages = {{2035--2104}}, publisher = {{Annals of Mathematics}}, title = {{{On the negative Pell equation}}}, doi = {{10.4007/annals.2010.172.2035}}, volume = {{172}}, year = {{2010}}, } @inproceedings{11939, abstract = {{In this paper a switching linear dynamical model (SLDM) approach for speech feature enhancement is improved by employing more accurate models for the dynamics of speech and noise. The model of the clean speech feature trajectory is improved by augmenting the state vector to capture information derived from the delta features. Further a hidden noise state variable is introduced to obtain a more elaborated model for the noise dynamics. Approximate Bayesian inference in the SLDM is carried out by a bank of extended Kalman filters, whose outputs are combined according to the a posteriori probability of the individual state models. Experimental results on the AURORA2 database show improved recognition accuracy.}}, author = {{Windmann, Stefan and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008)}}, keywords = {{a posteriori probability, AURORA2 database, Bayesian inference, Bayes methods, channel bank filters, extended Kalman filter banks, hidden noise state variable, Kalman filters, noise dynamics, speech enhancement, speech feature enhancement, speech feature trajectory, switching linear dynamical model approach}}, pages = {{4409--4412}}, title = {{{Modeling the dynamics of speech and noise for speech feature enhancement in ASR}}}, doi = {{10.1109/ICASSP.2008.4518633}}, year = {{2008}}, } @inproceedings{11824, abstract = {{Soft-feature based speech recognition, which is an example of uncertainty decoding, has been proven to be a robust error mitigation method for distributed speech recognition over wireless channels exhibiting bit errors. In this paper we extend this concept to packet-oriented transmissions. The a posteriori probability density function of the lost feature vector, given the closest received neighbours, is computed. In the experiments, the nearest frame repetition, which is shown to be equivalent to the MAP estimate, outperforms the MMSE estimate for long bursts. Taking the variance into account at the speech recognition stage results in superior performance compared to classical schemes using point estimates. A computationally and memory efficient implementation of the proposed packet loss compensation scheme based on table lookup is presented}}, author = {{Ion, Valentin and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}}, keywords = {{distributed speech recognition, least mean squares methods, MAP estimate, maximum likelihood estimation, MMSE estimate, packet loss compensation scheme, packet switched communication, posteriori probability density function, robust error mitigation method, soft-features, speech recognition, table lookup, voice communication, wireless channels}}, pages = {{I}}, title = {{{An Inexpensive Packet Loss Compensation Scheme for Distributed Speech Recognition Based on Soft-Features}}}, doi = {{10.1109/ICASSP.2006.1659984}}, volume = {{1}}, year = {{2006}}, } @article{6072, abstract = {{According to the concept of direct parameter specification, nonconsciously registered information can be processed to the extent that it matches currently active intentions of a person. This prediction was tested and confirmed in the current study. Masked visual information provided by peripheral cues led to reaction time (RT) effects only if the information specified one of the required responses (Experiments 1 and 3). Information delivered by the same masked cues that did not match the intentions was not used. However, the same information influenced RT if it was provided by visible cues (Experiments 2 and 3). The results suggest that the processing of nonconsciously registered information is flexible because it is susceptible to the changing intentions of a person. Yet, these processes are apparently restricted as nonconsciously registered information cannot be used as easily for purposes not corresponding to the currently active intentions as better visible information. (PsycINFO }}, author = {{Ansorge, Ulrich and Heumann, Manfred and Scharlau, Ingrid}}, issn = {{1053-8100}}, journal = {{Consciousness and Cognition: An International Journal}}, keywords = {{active intentions, cues, direct parameter specification, nonconscious processing ability, Adult, Consciousness, Female, Humans, Male, Mental Processes, Perceptual Masking, Photic Stimulation, Visual Perception, Awareness, Cognitive Processes, Cues, Intention, Consciousness States, Probability}}, number = {{4}}, pages = {{528 -- 545}}, title = {{{Influences of visibility, intentions, and probability in a peripheral cuing task.}}}, volume = {{11}}, year = {{2002}}, } @article{40334, author = {{Kitzerow, Heinz-Siegfried and Jérôme, B. and Pieranski, P.}}, issn = {{0378-4371}}, journal = {{Physica A: Statistical Mechanics and its Applications}}, keywords = {{Condensed Matter Physics, Statistics and Probability}}, number = {{1}}, pages = {{163--194}}, publisher = {{Elsevier BV}}, title = {{{Strain-induced anchoring transitions}}}, doi = {{10.1016/0378-4371(91)90423-a}}, volume = {{174}}, year = {{1991}}, } @article{40218, author = {{Lasser, R. and Rösler, Margit}}, issn = {{0304-4149}}, journal = {{Stochastic Processes and their Applications}}, keywords = {{Applied Mathematics, Modeling and Simulation, Statistics and Probability}}, number = {{2}}, pages = {{279--293}}, publisher = {{Elsevier BV}}, title = {{{Linear mean estimation of weakly stationary stochastic processes under the aspects of optimality and asymptotic optimality}}}, doi = {{10.1016/0304-4149(91)90095-t}}, volume = {{38}}, year = {{1991}}, }