Man-machine dialogue system optimization based on cloud computing

This paper studies the optimization and implementation of human-machine dialog system based on cloud computing technology. Firstly, the coarse-perceived hash generation based on the formant frequency is studied, and the detail-aware hash generation based on the energy difference in the time domain is studied. Then, the combination of coarse-perceived hash and detail-aware hash is elaborated. Secondly, the algorithm is tested and emulated. The algorithm matches the coarse-perceived hash sequence with fewer bits, and returns the voice number with similar rough features to the index voice after matching successfully, and matches the detail-aware hash sequence of the corresponding number voice. Then, the exact match of the result is gotten. Finally, it is concluded that the algorithm in this paper can effectively recognize the human voice.