Sixth European Conference on Speech Communication and Technology
(EUROSPEECH'99)

Budapest, Hungary
September 5-9, 1999

A Hybrid ANN/HMM Syllable Recognition Module Based on Vowel Spotting

John Sirigos, Nikos Fakotakis, George Kokkinakis

Wire Communications Laboratory, University of Patras, Greece

This paper presents a hybrid ANN/HMM syllable recognition module based on vowel spotting. An advanced multi-level vowel spotting method is used to achieve minimum vowel loss and accurate detection of the vowel location and duration. Discrete Hidden Markov Models (DSHMM), Multi Layer Perceptrons (MLP) and Heuristics (HR) are used for this purpose. A hybrid ANN/HMM technique is then used to recognize The syllables between the detected vowels. We replace the usual DSHMM probability parameters with combined neural network outputs. For this purpose both context dependent (CD) and context independent (CI) neural networks are used. Global normalization is employed on the parameters as opposed to the local normalization used on parameters in standard HMMs. Also, all parameters are estimated simultaneously according to the discriminative conditional maximum likelihood (CML) criterion. The tests were performed on the TIMIT and NTIMIT databases and showed significant performance improvement compared to similar systems.


Full Paper (PDF)

Bibliographic reference.  Sirigos, John / Fakotakis, Nikos / Kokkinakis, George (1999): "A hybrid ANN/HMM syllable recognition module based on vowel spotting", In EUROSPEECH'99, 1119-1122.