Small-Footprint Automatic Speech Recognition System using Two-Stage Transfer Learning based Symmetrized Ternary Weight Network

Abstract

Traditional automatic speech recognition (ASR) models face challenges when deployed on edge devices due to their high computational requirements and storage demands. To address this issue, we present a novel ASR system specifically designed for edge applications, encompassing both keyword spotting (KWS) and speaker verification (SV) functionalities with on chip learning for speaker registration. Our proposed system employs a compact model trained using a two-stage transfer learning method for on-the-fly small-sample speaker registration. In the proposed model, sparsity-controllable weights are symmetrically ternary-quantized to further exploit data reuse. Additionally, we introduce a Huffman-coding based weight lossy compression method to achieve efficient storage compaction. Moreover, we propose a specialized classifier taking the signal-to-noise ratio into account to enhance the accuracy of SV. The proposed ASR system has been successfully deployed on a 1.65mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> custom chip fabricated under 28-nm technology, with only 10.84KB of on-chip memory. This compact system effectively handles KWS and SV tasks, as well as on-chip speaker registration.