Gaussian Selection with Non-Overlapping Clusters for ASR in Embedded Devices

In this paper we propose a memory efficient version of the Gaussian Selection (GS) scheme, which is used for speeding up the likelihood calculations of an ASR system. The memory savings are achieved by using non-overlapping (disjoint) clusters instead of the overlapping clusters normally used in GS. As we will show, the new scheme achieves 66% computational savings with a relative increase in word error rate (WER) of 4%. We will also show, that combining the new GS scheme with frame rate reduction and feature masking provides further savings in computation. 75% (4% increase in WER) and 68% (3.5% increase in WER) savings were obtained by adding frame rate reduction and feature masking, respectively.