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  • Open Access


    Modified Viterbi Scoring for HMM‐Based Speech Recognition

    Jihyuck Joa, Han‐Gyu Kimb, In‐Cheol Parka, Bang Chul Jungc, Hoyoung Yooc

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 351-358, 2019, DOI: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. More >

  • Open Access


    Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model

    Junbiao Liu1, 6, Duanpo Wu2, 3, Zimeng Wang2, Xinyu Jin1, *, Fang Dong4, Lurong Jiang5, Chenyi Cai6

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 401-426, 2020, DOI:10.32604/cmes.2020.08731

    Abstract In the field of medical informatics, sleep staging is a challenging and timeconsuming task undertaken by sleep experts. According to the new standard of the American Academy of Sleep Medicine (AASM), the stages of sleep are divided into wakefulness (W), rapid eye movement (REM) and non-rapid eye movement (NREM) which includes three sleep stages (N1, N2 and N3) that describe the depth of sleep. This study aims to establish an automatic sleep staging algorithm based on the improved weighted random forest (WRF) and Hidden Markov Model (HMM) using only the features extracted from double-channel EEG… More >

  • Open Access


    A Face Recognition Algorithm Based on LBP-EHMM

    Tao Li1, Lingyun Wang1, Yin Chen1,*, Yongjun Ren1, Lei Wang1, Jinyue Xia2

    Journal on Artificial Intelligence, Vol.1, No.2, pp. 59-68, 2019, DOI:10.32604/jai.2019.06346

    Abstract In order to solve the problem that real-time face recognition is susceptible to illumination changes, this paper proposes a face recognition method that combines Local Binary Patterns (LBP) and Embedded Hidden Markov Model (EHMM). Face recognition method. The method firstly performs LBP preprocessing on the input face image, then extracts the feature vector, and finally sends the extracted feature observation vector to the EHMM for training or recognition. Experiments on multiple face databases show that the proposed algorithm is robust to illumination and improves recognition rate. More >

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