The aim of this work is to show the ability of stochastic regular grammars to generate accurate language models which can be well integrated, allocated and handled in a continuous speech recognition system. For this purpose, a syntactic version of the well-known n-gram model, called k-testable language in the strict sense (k-TSS), is used. The complete definition of a k-TSS stochastic finite state automaton is provided in the paper. One of the difficulties arising in representing a language model through a stochastic finite state network is that the recursive schema involved in the smoothing procedure must be adopted in the finite state formalism to achieve an efficient implementation of the backing-off mechanism. The use of the syntactic back-off smoothing technique applied to k-TSS language modelling allowed us to obtain a self-contained smoothed model integrating several k-TSS automata in a unique smoothed and integrated model, which is also fully defined in the paper. The proposed formulation leads to a very compact representation of the model parameters learned at training time: probability distribution and model structure. The dynamic expansion of the structure at decoding time allows an efficient integration in a continuous speech recognition system using a one-step decoding procedure.
An experimental evaluation of the proposed formulation was carried out on two Spanish corpora. These experiments showed that regular grammars generate accurate language models (k-TSS) that can be efficiently represented and managed in real speech recognition systems, even for high values of k, leading to very good system performance.