Continuous speech recognition with neural networks and stationary-transitional acoustic units

This paper proposes the use of a kind of acoustic units named stationary-transitional units within a hybrid hidden Markov model/neural network recognition framework as an alternative to standard context-independent phonemes. These units are made up of stationary parts of the context independent phonemes plus all the admissible transitions between them and represent a partition of the sounds of the language, like phonemes, but with more acoustic detail. These units are very suitable to be modeled with neural networks and their use may enhance the performances of hybrid HMM-NN systems by increasing their acoustic resolution. This hypothesis is verified for the Italian language, experimenting these units on a difficult domain of spontaneous speech recognition, namely railway timetable vocal access with the Dialogos system. The results show that a relevant improvement is achieved with respects to the use of the standard context independent phonemes.