Paper Info
Title
Full Covariance Gaussian Mixture Models Evaluation On Gpu
Abstract
Gaussian mixture models (GMMs) are often used in various data processing and classification tasks to model a continuous probability density in a multi-dimensional space. In cases, where the dimension of the feature space is relatively high (e. g. in the automatic speech recognition (ASR)), GMM with a higher number of Gaussians with diagonal covariances (DC) instead of full covariances (FC) is used from the two reasons. The first reason is a problem how to estimate robust FC matrices with a limited training data set. The second reason is a much higher computational cost during the GMM evaluation. The first reason was addressed in many recent publications. In contrast, this paper describes an efficient implementation on Graphic Processing Unit (GPU) of the FC-GMM evaluation, which addresses the second reason. The performance was tested on acoustic models for ASR, and it is shown that even a low-end laptop GPU is capable to evaluate a large acoustic model in a fraction of the real speech time. Three variants of the algorithm were implemented and compared on various GPUs: NVIDIA CUDA, NVIDIA OpenCL, and ATIIAMD OpenCL.
Year
Venue
Keywords
2012
2012 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT)
Gaussian Mixture Models, Full Covariance, Automatic Speech Recognition, GPU, CUDA, OpenCL
Field
DocType
ISSN
Signal processing,Feature vector,Pattern recognition,Computer science,CUDA,Artificial intelligence,Gaussian process,Probability density function,Mixture model,Covariance,Acoustic model
Conference
2162-7843
Citations 
PageRank 
References 
4
0.46
11
Authors
4
Name
Order
Citations
PageRank
Jan Vanek1459.10
Jan Trmal223520.91
Josef V. Psutka310218.39
Josef Psutka436555.24