Voice identification algorithm and voice identification module of modular robot

Abstract

The invention discloses a voice identification algorithm of a modular robot. The voice identification algorithm comprises the following steps: S1, performing interval sampling on a voice feature function f(x) to obtain m function values (X0(E), X1(E), L, Xm(E)); S2, converting the feature function f(X)={(X0(E), X1(E), L, Xm(E)} in a time domain into points in an m-dimensional Hilbert space by taking X0, X1, L and Xm as coordinates in the Hilbert space, and converting a voice sampling function family F(x) into a series of points in the Hilbert space; S3, analyzing a similarity relation among the series points of F(x) by taking the Hilbert space as a new feature space of a voice signal, and obtaining a mode identification module by applying a high-dimensional hypersphere covering method; S4, performing mode identification on the voice signal in the Hilbert space to complete voice identification. The voice identification algorithm of the modular robot has the most important characteristics that no FFT is provided, the complexity is low, and the algorithm is simple; the voice identification algorithm is suitable for a voice control system of a smaller-sized modular robot.

Description

technical field [0001] The invention relates to the technical field of speech recognition, in particular to a modular robot speech recognition algorithm and a speech recognition module thereof. Background technique [0002] After nearly fifty years of development, speech recognition technology has shown great application prospects in many application fields. Speech recognition belongs to the category of multi-dimensional pattern recognition and intelligent computer interface. It is a comprehensive technology integrating acoustics, phonetics, computer, information processing, artificial intelligence and other fields. It has been widely used in various fields such as industry, military, transportation, medicine and civilian use. Applications, such as automatic voice information acquisition systems, language translation systems, robot systems, voice-activated telephone exchanges, voice dialing systems, information network inquiries, home services, dictation machines, computer c...

AI Technical Summary

Problems solved by technology

At present, the key technologies of most speech recognition systems are developed based on the HMM model. HMM is a statistical model. The estimation of model parameters requires a large amount of training data, and the training process is computationally intensive, time-consuming, and extremely complicated. It is impossible to run on general hardware

For the voice control part of the modular robot, the speech recognition algorithm is complex, and its accuracy rate of speech recognition is not high, and it can only be vaguely recognized according to the level of the voice.

In the Chinese invention patent whose application publication number is CN 103971676A, a fast speech isolated word recognition algorithm is disclosed, which uses the characteristic parameters of the zero-crossing rate speech signal to extract, which better solves the problem of characteristic parameter extraction, but has not yet Solve the problems of small robot voice recognition modularization

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