Robustness feature extraction method based on logarithmic spectrum noise-to-signal weighting

Abstract

The invention discloses a robustness feature extraction method based on logarithmic spectrum noise-to-signal weighting. First of all, a short-time Mel sub-band spectrum of each frame is obtained by performing acoustic preprocessing, short-time spectrum estimation and Mel filtering on input voice; then a logarithmic spectrum is obtained by performing nonlinear transformation on the Mel sub-band spectra by use of an improved logarithm function, at the same, a logarithmic spectrum domain signal-to-noise ratio of the input voice is estimated from the Mel sub-band spectra; then, a weighted logarithmic spectrum is obtained by performing weighting on the logarithmic spectrum of the input voice by use of the estimated logarithmic spectrum domain posterior signal-to-noise ratio; and finally, feature parameters of the input voice are obtained by performing discrete cosine transform and time domain differentiating on the weighted logarithmic spectrum. The method improves the environment robustness of the feature parameters extracted in a noise environment, reduces influences exerted by additive noise on a voice identification system and also has the advantages of quite small calculation amount and easy real-time realization.

Description

technical field [0001] The present invention relates to the use of an improved logarithmic function to carry out nonlinear transformation on the Mel spectrum of speech, and to weight the logarithmic spectrum after the nonlinear transformation with the logarithmic spectrum a posteriori signal-to-noise ratio to reduce the impact of noise on the speech recognition system. A stick feature extraction method belongs to the technical field of speech recognition. Background technique [0002] Due to the variability of speech signals, the recognition performance of speech recognition systems in real environments may deteriorate dramatically. Additive background noise, linear channel distortion and speaker change are the most important factors causing speech variation. Generally, the impact of speech mismatch can be reduced from three aspects: one is to extract noise-insensitive anti-noise feature parameters; the other is to estimate pure speech features from noisy speech to match th...

AI Technical Summary

Problems solved by technology

In MFCC, the smaller input value is exactly the Mel subband speech with smaller energy, and they are easily affected by additive noise

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