Digital speech perception hash method based on formant frequency

Abstract

The invention discloses a digital speech perception hash method based on formant frequency. The method is used for speech retrieval in a big data background, and the format frequency capable of reflecting timbre characteristics of speakers and time domain energy differences having the strong robustness can be respectively extracted to be used as the detail characteristics of the speech segments. During the matching process, the speech rough characteristics can be matched, and the speech segments having the timbres, which are similar to that of the target speech, can be screened out, and then the speeches having the similar timbres can be screened out for the matching of the detail characteristics, and at last, the accurate matching result can be acquired. When the method is used for the mass speech signal processing, a lot of unnecessary calculation amount can be saved, and the matching efficiency can be improved obviously.

Description

technical field [0001] The invention relates to a novel voice perception hash scheme aiming at how to improve the processing efficiency of massive voice signals under the background of big data application. Background technique [0002] With the advent of the "Internet +" era, the rapid development of mobile Internet, cloud computing, big data and artificial intelligence, people are eager to interact with computers directly through voice, which makes large-scale storage and processing of voice a research hotspot. As the technical support of information services such as multimedia content identification, retrieval, and authentication, perceptual hashing will face two major problems: how to reduce computational complexity and computational efficiency. The current perceptual hashing algorithm mainly focuses on the performance of feature extraction methods and hash construction methods, without considering the efficiency and complexity of applying perceptual hashing in practice ...

AI Technical Summary

Problems solved by technology

Although the performance of the existing perceptual hashing algorithm is constantly improving, it has not been considered from the perspective of big data application background, and the balance between algorithm complexity and practical application efficiency cannot be achieved

[0004] To sum up, the current perceptual hashing algorithm mainly focuses on the performance of feature extraction methods and hash construction methods, without considering the efficiency and complexity of applying perceptual hashing in practice in the context of big data

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