Abstract
This paper proposes a new discrete speech recognition method which investigates the capability of graphical models based on tree distributions that are widely used in many optimization areas. A novel spanning tree structure that utilizes the temporal nature of speech signal is proposed. The proposed tree structure significantly reduces complexity in so far that can reflect simply a few essential relationships rather than all possible structures of trees. The application of this model is illustrated with different isolated word databases. Experimentally it has been shown that, the proposed approaches compared to the conventional discrete hidden Markov model (DHMM) yield reduced error rates of 2.54 %–12 % and improve recognition speed minimum 3-fold. In addition, an impressive gain in learning time is observed. The overall recognition accuracy was 93.09 %–95.34 %, thereby confirming the effectiveness of the proposed methods.
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Hammami, N., Bedda, M. & Farah, N. Tree distributions approximation model for robust discrete speech recognition. Int J Speech Technol 15, 455–462 (2012). https://doi.org/10.1007/s10772-012-9141-9
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DOI: https://doi.org/10.1007/s10772-012-9141-9