Isolated Word Recognition by Recursive HMM Parameter Estimation Algorithm


  • Jūratė Vaičiulytė Faculty of Electronics and Informatics, Vilniaus Kolegija – University of Applied Sciences, Vilnius, Lithuania
  • Leonidas Sakalauskas Vilnius University Šiauliai Academy, Šiauliai, Lithuania



Hidden Markov models, likelihood method, on-line algorithm, recursive EM algorithm, isolated word recognition


Automatic speech recognition (ASR) technologies enable humans to communicate with computers. Isolated word recognition (IWR) is an important part of many known ASR systems. Minimizing the word error rate in cases of incremental learning is a unique challenge for developing an on-line ASR system. This paper focuses on on-line IWR using a recursive hidden Markov model (HMM) multivariate parameter estimation algorithm. The maximum likelihood method was used to estimate the unknown parameters of the model, and an algorithm for the adapted recursive EM algorithm for HMMs parameter estimation was derived. The resulting recursive EM algorithm is unique among its counterparts because of state transition probabilities calculation. It obtains more accurate parameter estimates compared to other algorithms of this type. In our experiment, the algorithm was implemented and adapted to several datasets for IWR. Thus, the recognition rate and algorithm convergence results are discussed in this work.


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How to Cite

Vaičiulytė, J., & Sakalauskas, L. (2021). Isolated Word Recognition by Recursive HMM Parameter Estimation Algorithm. COMPUTING AND INFORMATICS, 40(2), 277–297.