  We propose a stacked 1D convolutional neural network (S1DCNN) for end-to-end
small footprint voice trigger detection in a streaming scenario. Voice trigger
detection is an important speech application, with which users can activate
their devices by simply saying a keyword or phrase. Due to privacy and latency
reasons, a voice trigger detection system should run on an always-on processor
on device. Therefore, having small memory and compute cost is crucial for a
voice trigger detection system. Recently, singular value decomposition filters
(SVDFs) has been used for end-to-end voice trigger detection. The SVDFs
approximate a fully-connected layer with a low rank approximation, which
reduces the number of model parameters. In this work, we propose S1DCNN as an
alternative approach for end-to-end small-footprint voice trigger detection. An
S1DCNN layer consists of a 1D convolution layer followed by a depth-wise 1D
convolution layer. We show that the SVDF can be expressed as a special case of
the S1DCNN layer. Experimental results show that the S1DCNN achieve 19.0%
relative false reject ratio (FRR) reduction with a similar model size and a
similar time delay compared to the SVDF. By using longer time delays, the
S1DCNN further improve the FRR up to 12.2% relative.
