NVIDIA CUDA application to train and decode the Hidden Markov Models

The urgency of the discussed issue is caused by the need of optimization of huge speech corpus's processing algorithms required for developing robust automatic speech recognition systems. The evolution of modern multicore processors, specifically graphical processor units GPU, allows improving...

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Bibliographic Details
Published inIzvestiâ Tomskogo politehničeskogo universiteta. Inžiniring georesursov Vol. 324; no. 5
Main Authors Pavel Zatsepin, Denis Gefke
Format Journal Article
LanguageRussian
Published Tomsk Polytechnic University 01.05.2019
Subjects
Baum-Welch re-estimation algorithm
Hidden Markov Models
NVIDIA CUDA
parallel computing
speech recognition
Viterbi algorithm
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Summary:The urgency of the discussed issue is caused by the need of optimization of huge speech corpus's processing algorithms required for developing robust automatic speech recognition systems. The evolution of modern multicore processors, specifically graphical processor units GPU, allows improving sufficiently the performance of difficult and resource-intensive digital signal processing algorithms and reducing sufficiently a data processing time. The main aim of the study is to optimize education (Baum-Welch re-estimation) and decoding (Viterbi) algorithms of Hidden Markov Models by parallel programming technology NVIDIA CUDA and to estimate performance increase in comparison within the CPU. The methods used in the study: the search of education and decoding algorithm's parts suitable for effective parallel realization by NVIDIA CUDA and its implementation. The results: The authors have developed parallel realization of education and decoding Hidden Markov Models algorithms by GPU and have estimated the performance increase in comparison within the CPU for different model's parameters (the number of model state and dimension of a feature vector). The results of the paper can be used both by engineers developing and improving the automatic speech recognition systems and by explorers working on a digital signal processing and artificial intelligence systems.
ISSN:2500-1019
2413-1830