The large size of Weighted Finite-State Transducers (WFSTs) used in Automatic Speech Recognition (ASR), such as the language model or integrated networks, is an important problem for many ASR applications. To address this problem, we present a general purpose compression technique for WFSTs that is specially designed for the finite-state machines most commonly used in ASR. Experiments run on 2 large tasks show the method to be very effective, typicality reducing memory and disk requirements to less than 35%. By combining it with "on-the-fly" composition, the memory requirements are further reduced to below 14%. These reductions show no negative impact on recognition speed.
Bibliographic reference. Caseiro, Diamantino (2010): "WFST compression for automatic speech recognition", In INTERSPEECH-2010, 1493-1496.