Development of HMM/Neural Network-Based Medium-Vocabulary Isolated-Word Lithuanian Speech Recognition System

  • Authors:

  • Mark Filipovič;Antanas Lipeika

  • Affiliations:

  • Recognition Processes Department, Institute of Mathematics and Informatics, Goštauto 12-204, LT-01108 Vilnius, Lithuania, e-mail: markas@mch.mii.lt, lipeika@ktl.mii.lt;Recognition Processes Department, Institute of Mathematics and Informatics, Goštauto 12-204, LT-01108 Vilnius, Lithuania, e-mail: markas@mch.mii.lt, lipeika@ktl.mii.lt

  • Venue:
  • Informatica
  • Year:
  • 2004

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Abstract

The development of Lithuanian HMM/ANN speech recognition system, which combines artificial neural networks (ANNs) and hidden Markov models (HMMs), is described in this paper. A hybrid HMM/ANN architecture was applied in the system. In this architecture, a fully connected three-layer neural network (a multi-layer perceptron) is trained by conventional stochastic back-propagation algorithm to estimate the probability of 115 context-independent phonetic categories and during recognition it is used as a state output probability estimator. The hybrid HMM/ANN speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using CSLU Toolkit. The system was tested on the VDU isolated-word Lithuanian speech corpus and evaluated on a speaker-independent ∼750 distinct isolated-word recognition task. The word recognition accuracy obtained was about 86.7%.