KASP: A 96.8% 10-Keyword Accuracy and 1.68µJ/Classification Keyword Spotting and Speaker Verification Processor Using Adaptive Beamforming and Progressive Wake-Up

📋 论文概要

提出了一种名为KASP的关键词识别与说话人验证联合处理器，通过自适应噪声抑制技术解决了传统KWS处理器对环境噪声敏感的问题，实现了96.8%的10关键词识别准确率和1.68µJ/分类的超低功耗。

💡 主要创新点

• 首次将关键词识别与说话人验证集成在同一处理器中，实现联合任务处理。
• 采用自适应噪声抑制算法，有效降低人类语音噪声（如旁人交谈、电视声音）对识别准确率的影响。
• 通过硬件架构优化实现极低功耗，每分类仅消耗1.68µJ能量。

🏷 关键词

📄 原文摘要

Chunsheng Ji1, Yu Long1, Xiao Chen2, Xiaoyu Miao2, Liang Zhou1, Liang Chang1, Shanshan Liu1, Jun Zhou1 University of Electronic Science and Technology of China, Chengdu, China China Micro Semicon, Chengdu, China 1 2 Keyword spotting (KWS) processors have been proposed and used in voice-control applications such as smart homes, intelligent robots and smart wearables, as shown in Fig. 14.8.1. Existing KWS processors have the following issues: 1) they are sensitive to human-voice noise (e.g., nearby individuals talking, TV or radio), which affects their accuracy in real-life applications; 2) they do not sufficiently exploit domain-specific features for energy reduction and accuracy improvement; 3) they do not support multiuser speaker verification (SV) free of speaker-specific training. To address these issues, in this work, we have proposed a high accuracy and ultra-energy-efficient KWS & SV processor (named KASP) with the following features: 1) a dynamically reconfigurable

👥 作者与机构

Jianbiao Xiao1, Xuhui Zhang1, Shijian Zhu1, Zhengwei Yang1, Meng Du1,