A hybrid syllable recognition system based on vowel spotting

  • Authors:

  • John Sirigos;Nikos Fakotakis;George Kokkinakis

  • Affiliations:

  • Wire Communications Laboratory, University of Patras, 26500 Patras, Greece;Wire Communications Laboratory, University of Patras, 26500 Patras, Greece;Wire Communications Laboratory, University of Patras, 26500 Patras, Greece

  • Venue:
  • Speech Communication
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper we present a hybrid ANN/HMM syllable recognition system based on vowel spotting. Using an advanced multilevel vowel-spotting module we track all vowel phonemes in speech signals from where we model the speech segments located between two successive vowels which are defined as syllables. In order to achieve minimum vowel losses and accurate detection, we focus on taking special care of the vowel spotter which is based on three different techniques: discrete hidden Markov models (DHMMs), multilayer perceptrons and heuristic rules.To set up the models of the syllable segments, hybrid DHMMs with multiple codebooks are used. The usual DHMM probability parameters are replaced by combined neural network outputs. For this purpose, we use both context dependent and context independent neural networks.The syllable recognition system was tested with the TIMIT and NTIMIT databases and the results obtained showed 75.09% and 59.30% average syllable recognition accuracy, respectively. It has to be noted that to achieve the above results no grammars or syllable-based lexicons were used.