Zobrazeno 1 - 10
of 40
pro vyhledávání: '"J.R. Rohlicek"'
Publikováno v:
IEEE Transactions on Speech and Audio Processing. 1:431-442
A nontraditional approach to the problem of estimating the parameters of a stochastic linear system is presented. The method is based on the expectation-maximization algorithm and can be considered as the continuous analog of the Baum-Welch estimatio
Publikováno v:
IEEE Signal Processing Letters. 2:157-159
A dependence tree is a model for the joint probability distribution of an n-dimensional random vector, which requires a relatively small number of free parameters by making Markov-like assumptions on the tree. The authors address the problem of maxim
Publikováno v:
ICASSP
A word-spotting system using Gaussian hidden Markov models is presented. Several aspects of this problem are investigated. Specifically, results are reported on the use of various signal processing and feature transformation techniques. The authors h
Publikováno v:
ICASSP
Statistical language models have been successfully used to improve the performance of continuous speech recognition algorithms. Application of such techniques is difficult when only a small training corpus is available. The authors present an approac
Autor:
Ming-Whei Feng, Yen-Lu Chow, J. Makhoul, R. Schwartz, J. Vandegrift, A Derr, S. Roucos, J.R. Rohlicek, Francis Kubala, Patti Price, Owen Kimball
Publikováno v:
ICASSP
The system was trained in a speaker dependent mode on 28 minutes of speech from each of 8 speakers, and was tested on independent test material for each speaker. The system was tested with three artificial grammars spanning a broad perplexity range.
Publikováno v:
IEEE Transactions on Speech and Audio Processing. 2:453-455
Describes a method for clustering multivariate Gaussian distributions using a maximum likelihood criterion. The authors point out possible applications of model clustering, and then use the approach to determine classes of shared covariances for cont
Autor:
Mari Ostendorf, J.R. Rohlicek
Publikováno v:
ICASSP
An approach that involves designing a vector quantizer to maximize the mutual information between the hidden Markov model (HMM) states and the quantized observations is presented. The iterative design of the quantizer and the HMM parameters is shown
Publikováno v:
ICASSP
A conditional probability model is developed for relating a noisy, observation feature vector to the noise-free vector that generated it. The model is a Gaussian mixture which is based on the vectors and is conditioned on the instantaneous signal-to-
Publikováno v:
ICASSP
The design of search algorithms is an important issue in large vocabulary speech recognition, especially as more complex models are developed for improving recognition accuracy. Multi-pass search strategies have been used as a means of applying simpl
Publikováno v:
3rd European Conference on Speech Communication and Technology (Eurospeech 1993).