Dynamic speech emotion recognition with state-space models
暂无分享,去创建一个
Gareth W. Peters | Tomoko Matsui | Konstantin Markov | François Septier | G. Peters | F. Septier | T. Matsui | K. Markov
[1] Carl E. Rasmussen,et al. State-Space Inference and Learning with Gaussian Processes , 2010, AISTATS.
[2] Nando de Freitas,et al. Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.
[3] Carl E. Rasmussen,et al. Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC , 2013, NIPS.
[4] Ieee Staff. 2017 25th European Signal Processing Conference (EUSIPCO) , 2017 .
[5] Carl E. Rasmussen,et al. Robust Filtering and Smoothing with Gaussian Processes , 2012, IEEE Transactions on Automatic Control.
[6] Dieter Fox,et al. GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models , 2008, IROS.
[7] Heng Wang,et al. Depression recognition based on dynamic facial and vocal expression features using partial least square regression , 2013, AVEC@ACM Multimedia.
[8] Björn W. Schuller,et al. Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies , 2008, INTERSPEECH.
[9] Björn Schuller,et al. Opensmile: the munich versatile and fast open-source audio feature extractor , 2010, ACM Multimedia.
[10] S. Haykin. Kalman Filtering and Neural Networks , 2001 .
[11] Youngmoo E. Kim,et al. Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering , 2010, 2010 Ninth International Conference on Machine Learning and Applications.
[12] Christopher K. I. Williams,et al. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .
[13] Jongmin Kim,et al. Phoneme Classification using Constrained Variational Gaussian Process Dynamical System , 2012, NIPS.
[14] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[15] David J. Fleet,et al. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .
[16] Gustav Eje Henter,et al. Gaussian process dynamical models for nonparametric speech representation and synthesis , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[17] Tomoko Matsui,et al. Music Genre and Emotion Recognition Using Gaussian Processes , 2014, IEEE Access.
[18] M. Deisenroth,et al. A general perspective on Gaussian filtering and smoothing: Explaining current and deriving new algorithms , 2011, Proceedings of the 2011 American Control Conference.
[19] Uwe D. Hanebeck,et al. Analytic moment-based Gaussian process filtering , 2009, ICML '09.
[20] Carl E. Rasmussen,et al. Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..
[21] Mohamed Chetouani,et al. Robust continuous prediction of human emotions using multiscale dynamic cues , 2012, ICMI '12.
[22] Björn W. Schuller,et al. AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge , 2014, AVEC '14.
[23] Nadia Bianchi-Berthouze,et al. Naturalistic Affective Expression Classification by a Multi-stage Approach Based on Hidden Markov Models , 2011, ACII.