Optimization and Evaluation of Energy-Efficient Mixed-Signal MFCC Feature Extraction Architecture
Yan‐Ming Zhang, Xu Yun Qiu, Qin Li, Fei Qiao, Qi Wei, Li Luo, Huazhong Yang · 2020
Speech feature extraction is an indispensable module in the whole speech recognition process. Especially in the energy-constrained internet of things nodes, low-power feature extraction greatly improves the working time of the system. This paper optimizes a complete energy-efficient speech feature extraction architecture in the mixed-signal domain for speech recognition. The speech feature extraction architecture extracts acoustic features in the mixed-signal domain, which significantly reduces the cost of Analog-to-Digital Converter (ADC) and computational complexity. Moreover, the noise robustness of the mixed-signal MFCC feature has been investigated to adapt to the real scene. In order to evaluate the performance of the proposed optimized architecture, we fabricate a chip about the proposed feature extraction architecture in 180nm CMOS process, the post-simulation results show that the core of mixed-signal MFCC feature extraction achieves 70% power saving and enhanced noise robustness than state of the art.