Performance comparison between semicontinuous and discrete hidden Markov models of speech

1988 ◽  
Vol 24 (3) ◽  
pp. 149 ◽  
Author(s):  
X.D. Huang ◽  
M.A. Jack
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Performance Comparison

scholarly journals FPGA implementation of logarithmic versions of Baum-Welch and Viterbi algorithms for reduced precision hidden Markov models

2017 ◽  
Vol 65 (6) ◽  
pp. 935-947
Author(s):  
M. Pietras ◽  
P. Klęsk
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Hardware Acceleration ◽  
Performance Comparison ◽  
Divide And Conquer ◽  
Floating Point ◽  
Logarithmic Space ◽  
On Chip ◽  
High Level

Abstract This paper presents a programmable system-on-chip implementation to be used for acceleration of computations within hidden Markov models. The high level synthesis (HLS) and “divide-and-conquer” approaches are presented for parallelization of Baum-Welch and Viterbi algorithms. To avoid arithmetic underflows, all computations are performed within the logarithmic space. Additionally, in order to carry out computations efficiently – i.e. directly in an FPGA system or a processor cache – we postulate to reduce the floating-point representations of HMMs. We state and prove a lemma about the length of numerically unsafe sequences for such reduced precision models. Finally, special attention is devoted to the design of a multiple logarithm and exponent approximation unit (MLEAU). Using associative mapping, this unit allows for simultaneous conversions of multiple values and thereby compensates for computational efforts of logarithmic-space operations. Design evaluation reveals absolute stall delay occurring by multiple hardware conversions to logarithms and to exponents, and furthermore the experiments evaluation reveals HMMs computation boundaries related to their probabilities and floating-point representation. The performance differences at each stage of computation are summarized in performance comparison between hardware acceleration using MLEAU and typical software implementation on an ARM or Intel processor.

scholarly journals Modern Computational Techniques for the HMMER Sequence Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiandong Meng ◽  
Yanqing Ji
Keyword(s):  
Data Analysis ◽  
Sequence Analysis ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Hardware Acceleration ◽  
Performance Comparison ◽  
Computational Techniques ◽  
Software And Hardware ◽  
Computing Platforms

This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications—hidden Markov models (HMM). We show the detailed performance comparison of sequence analysis tools on various computing platforms recently developed in the bioinformatics society. The characteristics of the sequence analysis, such as data and compute-intensive natures, make it very attractive to optimize and parallelize by using both traditional software approach and innovated hardware acceleration technologies.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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