@Article{2019.100000096,
AUTHOR = {Jihyuck Jo, Han‐Gyu Kim, In‐Cheol Park, Bang Chul Jung, Hoyoung Yoo},
TITLE = {Modified Viterbi Scoring for HMM‐Based Speech Recognition},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {2},
PAGES = {351--358},
URL = {http://www.techscience.com/iasc/v25n2/39662},
ISSN = {2326-005X},
ABSTRACT = {A modified Viterbi scoring procedure is presented in this paper based on 
Dijkstra’s shortest-path algorithm. In HMM-based speech recognition systems, 
the Viterbi scoring plays a significant role in finding the best matching model, 
but its computational complexity is linearly proportional to the number of 
reference models and their states. Therefore, the complexity is serious in 
implementing a high-speed speech recognition system. In the proposed 
method, the Viterbi scoring is translated into the searching of a minimum path, 
and the shortest-path algorithm is exploited to decrease the computational 
complexity while preventing the recognition accuracy from deteriorating. In 
addition, a two-phase comparison structure is proposed to manage state 
probabilities efficiently. Simulation results show that the proposed method 
saves computational complexity and recognition time by more than 21% and 
10% compared to the conventional Viterbi scoring and the previous early 
termination, respectively. The improvement of the proposed method becomes 
significant as the numbers of reference models, states, and Gaussian mixture 
models increase, which means that the proposed method is more desirable for 
recent speech recognition systems that deals with complex models.},
DOI = {10.31209/2019.100000096}
}