Research and Application of Power Dispatching Speech Recognition Based on Deep Neural Networks
Year: 2025
Type: article
Abstract: Due to the computational resource consumption of the decoding module and the impact of noisy environment on speech recognition system, this paper designs a real-time multi-stage speech preprocessing algorithm. The algorithm changes the traditional streaming recognition mode of detecting endpoints while recognizing. It reduces the decoding power consumption of the system by first performing endpoint detection and then decoding module. For wake-up decoding and recognition decoding, the real-time multilevel speech preprocessing algorithm significantly reduces the real-time rate and power consumption of the entire speech recognition system, and at the same time ensures that the impact on the wake-up rate and word error rate is negligible. The real-time rate is reduced by 27%-37% and the power consumption of the whole system is reduced by about 25%.
Source: IEEE Access
Authors: Haitao Yu, Xuqiang Wang, Jian Zheng, Xiaoxi Zhou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411948817