Open Access
ARTICLE
Modified Viterbi Scoring for HMM‐Based Speech Recognition
Jihyuck Joa, Han‐Gyu Kimb, In‐Cheol Parka, Bang Chul Jungc, Hoyoung Yooc
a School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea;
b School of Computer Science, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea;
c Department of Electronics Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea
* Corresponding Author: Hoyoung Yoo,
Intelligent Automation & Soft Computing 2019, 25(2), 351-358. https://doi.org/10.31209/2019.100000096
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.
Keywords
Cite This Article
J. Jo, H. Kim, I. Park, B. C. Jung and H. Yoo, "Modified viterbi scoring for hmm‐based speech recognition,"
Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 351–358, 2019. https://doi.org/10.31209/2019.100000096