In this work, voice activity detection (VAD) systems with system-level energy-quality (EQ) scaling are investigated. Compared to prior single-knob EQ scaling, multiple EQ knobs are selectively inserted into the entire signal chain from end to end. EQ knobs are dynamically co-optimized to minimize energy for a given quality target. The analysis shows that system-level EQ optimization provides several benefits and has interesting implications on the performance of machine learning-based classification, as exemplified by decision trees in this work. First, it can make quality degradation more graceful than single-knob, allowing for more aggressive energy reduction under a given quality target, while retaining the ability to operate at full quality. Also, proper system-level EQ optimization enhances fitting in machine learning-based systems (e.g., decision tree-based), suppressing both underfitting and overfitting. The analysis also shows that context-specific retraining significantly improves quality and resolves fitting issues, especially at low input SNR. Measurements on a 28nm testchip show that system-level EQ scaling can reduce energy by up to 3.5X at 2% accuracy degradation in 10-dB noise, compared to full quality. Iso-technology comparison shows that the minimum energy of 51.9 nJ/frame is lower than prior art by 1.9-74.4X at comparable speech/non-speech hit rates.

中文翻译：

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通过从数据转换到机器学习的能量质量扩展进行低能耗语音活动检测


在这项工作中，研究了具有系统级能量质量（EQ）缩放功能的语音活动检测（VAD）系统。与之前的单旋钮均衡器缩放相比，多个均衡器旋钮有选择地从一端到另一端插入到整个信号链中。 EQ 旋钮进行动态协同优化，以最大程度地减少给定质量目标的能量。分析表明，系统级 EQ 优化提供了多种好处，并对基于机器学习的分类的性能产生了有趣的影响，正如本工作中的决策树所例证的那样。首先，它可以使质量下降比单旋钮更加优雅，允许在给定质量目标下更积极地降低能量，同时保留全质量运行的能力。此外，适当的系统级 EQ 优化可以增强基于机器学习的系统（例如基于决策树）的拟合，从而抑制欠拟合和过拟合。分析还表明，特定环境的再训练可显着提高质量并解决拟合问题，尤其是在低输入 SNR 的情况下。 28nm 测试芯片上的测量表明，与全质量相比，系统级 EQ 缩放可在 10dB 噪声的精度降低 2% 的情况下将能耗降低高达 3.5 倍。等技术比较表明，在可比较的语音/非语音命中率下，51.9 nJ/帧的最小能量比现有技术低1.9-74.4倍。