초록 ▼

A method for recognizing a speaker in which a voice signal is spoken into a computer by a speaker and a feature vector is formed for the voice signal. The feature vector is compared to at least one stored reference feature vector and to at least one anti-feature vector. The reference feature vector

A method for recognizing a speaker in which a voice signal is spoken into a computer by a speaker and a feature vector is formed for the voice signal. The feature vector is compared to at least one stored reference feature vector and to at least one anti-feature vector. The reference feature vector is formed from a speech sample of a speaker to be verified. The anti-feature vector was formed from a speech sample that was spoken in by another speaker who is not the speaker to be verified. A 2-class classification is resolved by forming a similarity value and evaluating the similarity value on the basis of a predetermined range within which the similarity value must deviate from a predetermined value so that the voice signal can be classified as deriving from the speaker to be verified.

대표청구항 ▼

[ What is claimed is:] [1.]1. A method for verifying a person on the basis of voice signals using a neural network, the method comprising the steps of:in a training phase,(A) generating and storing a reference feature vector from training phase voice signal generated by a speaker to be verified,(B)

[ What is claimed is:] [1.]1. A method for verifying a person on the basis of voice signals using a neural network, the method comprising the steps of:in a training phase,(A) generating and storing a reference feature vector from training phase voice signal generated by a speaker to be verified,(B) generating and storing an anti-reference feature vector from a voice anti-signal generated by a speaker not to be verified,(C) training the neural network using the reference feature vector and the anti-reference feature vector, thereby adapting weightings of the neural network to permit an optimum classification for a two-class problem;and in an operating phase,(D) generating a feature vector from an operating phase voice signal generated by an unknown person, who may or may not be the speaker to be verified,(E) submitting the feature vector, the stored reference feature vector and the stored anti-feature vector to the neural network for a only a single comparison between (a) the feature vector and the reference feature vector and only a single comparison between (b) the feature vector and the anti-reference feature vector, wherein said comparisons are capable of being made when said anti-reference feature vector is generated from only one speaker not to be verified,(F) generating only a single operating phase similarity value from only the two comparisons in step (E), and(G) classifying the unknown person as verified when the single operating phase similarity value falls within a predetermined range of values.