Using smoothed K-TSS language models in continuous speech recognition

Abstract

A syntactic approach of the well-known N-grams models, the K-testable language in the strict sense (K-TSS), is used in this work to be integrated in a continuous speech recognition (CSR) system. The use of smoothed K-TSS regular grammars allowed to obtain a deterministic stochastic finite state automaton (SFSA) integrating K k-TSS models into a self-contained model. An efficient representation of the whole model in a simple array of adequate size is proposed. This structure can be easily handled at decoding time by a simple search function through the array. This formulation strongly reduced the number of parameters to be managed and thus the computing complexity of the model. An experimental evaluation of the proposed SFSA representation was carried out over an Spanish recognition task. These experiments showed important memory saving to allocate K-TSS language models, more important for higher values of K. They also showed that the decoding time did not meaningfully increased when K did. The lower word error rates for the Spanish task tested were achieved for K=4 and 5. As a consequence the ability of this syntactic approach of the N-grams to be well integrated in a CSR system, even for high values of K, has been established.