Hybrid HMM-NN modeling of stationary-transitional units for continuous speech recognition

This paper describes the benefits in recognition accuracy that can be achieved in a hybrid Hidden Markov Model-Neural Network (HMM-NN) recognition framework by using context-dependent subword units named Stationary - Transitional Units. These units are made up of stationary parts of the context-independent phonemes plus all the admissible transitions between them; they have good generalization capability and capture a wide acoustic detail. These units are very suitable to be modeled with neural networks, can enhance the performances of hybrid HMM-NN systems, and represent a real alternative to the context-independent phonemes. The efficacy of Stationary,Transitional Units is verified for the Italian language on isolated and continuous speech recognition tasks extracted from a real application employed for railway timetable telephonic vocal access. The results show that a relevant improvement is achieved with respect to the use of the context-independent phonemes. (C) 2000 Elsevier Science Inc. All rights reserved.