Results 151 to 160 of about 6,719 (190)

A Review of Deep Learning Techniques for EEG-Based Emotion Recognition: Models, Methods, and Datasets. [PDF]

F1000Res
Sreehari P, Raghavendra U, Gudigar A.
europepmc   +1 more source

CLAP-HMM: a biologically constrained deep learning framework for resistance gene prediction in long DNA sequences. [PDF]

BMC Biotechnol
Wang L, Xu Y, Guan X, Yan S.
europepmc   +1 more source

General Hidden Semi-Markov Model [PDF]

, 2016
Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs) provide flexible, general-purpose models for univariate and multivariate time series. Although interest in HMMs and HSMMs has continuously increased during the past years, and numerous articles on theoretical and practical aspects have been published, several gaps remain.
openaire   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Revisiting semi-continuous hidden Markov models

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
In the past decade, semi-continuous hidden Markov models (SCHMMs) have not attracted much attention in the speech recognition community. Growing amounts of training data and increasing sophistication of model estimation led to the impression that continuous HMMs are the best choice of acoustic model.
K. Riedhammer, T. Bocklet, A. Ghoshal, D. Povey   +3 more
openaire   +1 more source

On semi-continuous hidden Markov modeling

International Conference on Acoustics, Speech, and Signal Processing, 2002
The semicontinuous hidden Markov model is used in a 1000-word speaker-independent continuous speech recognition system and compared with the continuous mixture model and the discrete model. When the acoustic parameter is not well modeled by the continuous probability density, it is observed that the model assumption problems may cause the recognition ...
X. Huang, K.-F. Lee, H.-W. Hon
openaire   +1 more source

Hidden semi-Markov model for anomaly detection

Applied Mathematics and Computation, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tan, Xiaobin, Xi, Hongsheng
openaire   +1 more source

Machine condition recognition via hidden semi-Markov model

Computers & Industrial Engineering, 2021
Abstract In intelligent manufacturing systems, machines are subject to condition deterioration.Identifying machine condition is crucial for making practical decisions in production management. This paper studies the machine condition recognition problem in wafer fabrication.
Wenhui Yang, Lu Chen
openaire   +1 more source

Hidden semi-Markov model based speech synthesis

Interspeech 2004, 2004
In the present paper, a hidden-semi Markov model (HSMM) based speech synthesis system is proposed. In a hidden Markov model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions modeled by single Gaussian distributions.
Heiga Zen, Keiichi Tokuda, Takashi Masuko, Takao Kobayashi, Tadashi Kitamura   +4 more
openaire   +1 more source

Online identification of hidden Semi-Markov models

3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the, 2004
Hidden Markov models (HMM) are a powerful tool in signal modelling. In an HMM, the probability that signal leaves a state is constant, and hence the duration that signal stays in each state has an exponential distribution. However, this exponential density is not appropriate for a large class of physical signals.
M. Azimi, P. Nasiopoulos, R.K. Ward
openaire   +1 more source

Modeling state duration and emission dependence in hidden Markov and hidden semi-Markov models

2023
Hidden Markov models (HMM) are composed of a latent state sequence and an observation sequence conditionally independent on the states, which follows an emission distribution. Hidden semi-Markov models (HSMM) extend the HMM by explicitly modeling the duration in the states.
openaire   +2 more sources