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A ternary-weight neural network (TWN) inspired keyword spotting (KWS) processor is proposed to support complicated and variable application scenarios. To achieve high-precision recognition of ten keywords under 5 dB&#x007E;Clean wide range of background noises, a convolution neural network consists of four convolution layers and four fully connected layers, with modified sparsity-controllable truncated Gaussian approximation-based ternary-weight training is used. End-to-end optimization composed of three techniques is utilized: 1) the stage-by-stage bit-width selection algorithm to optimize the hardware overhead of FFT; 2) the lossy compressed TWN with symmetric kernel training (SKT) and dedicated internal data reuse computation flow; and 3) the error intercompensation approximate addition tree to reduce the computation overhead with marginal accuracy loss. Fabricated in an industrial 22-nm CMOS process, the processor realizes up to ten keywords in real-time recognition under 11 background noise types, with the accuracy of 90.6&#x0025;&#x0040;clean and 85.4&#x0025;&#x0040;5 dB. It consumes an average power of <inline-formula> <tex-math notation="LaTeX">$3.8 ~\mu \text{W}$ </tex-math></inline-formula> at 250 kHz and the normalized energy efficiency is <inline-formula> <tex-math notation="LaTeX">$2.79\times $ </tex-math></inline-formula> higher than state of the art.