  This paper proposes a novel pipeline for automatic grammar augmentation that
provides a significant improvement in the voice command recognition accuracy
for systems with small footprint acoustic model (AM). The improvement is
achieved by augmenting the user-defined voice command set, also called grammar
set, with alternate grammar expressions. For a given grammar set, a set of
potential grammar expressions (candidate set) for augmentation is constructed
from an AM-specific statistical pronunciation dictionary that captures the
consistent patterns and errors in the decoding of AM induced by variations in
pronunciation, pitch, tempo, accent, ambiguous spellings, and noise conditions.
Using this candidate set, greedy optimization based and cross-entropy-method
(CEM) based algorithms are considered to search for an augmented grammar set
with improved recognition accuracy utilizing a command-specific dataset. Our
experiments show that the proposed pipeline along with algorithms considered in
this paper significantly reduce the mis-detection and mis-classification rate
without increasing the false-alarm rate. Experiments also demonstrate the
consistent superior performance of CEM method over greedy-based algorithms.
