Stacked 1D Convolutional Networks for End-to-End Small Footprint Voice Trigger Detection
Takuya Higuchi, Mohammad Ghasemzadeh, Kisun You, Chandra Dhir · 2020
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.