With the development of artificial intelligence, the real-time feature extraction of acoustic signals is required in a wide variety of applications, such as keyword spotting and speech recognition. Feature extraction based on Mel-frequency cepstral coefficients (MFCCs) is one of the most significant methods thereinto. A software implementation of the MFCC extraction results in relatively high power consumption and computational time limitation, often making it unsuitable for tiny battery powered devices. Therefore, an on-chip accelerator of MFCC extraction is of interest in cutting-edge scenarios. This paper presents a fixed-point low-power hardware accelerator of MFCC feature extraction implemented in 22nm FDSOI technology. It consumes an average power of 2.78µW for 1024-sample frame at a clock frequency of 1MHz. For keyword spotting, the quantized accelerator achieves an average accuracy of around 96% working along with different classification networks.