Speaker recognition method based on twin network model and KNN (K-nearest neighbor) algorithm

Abstract

The invention discloses a speaker recognition method based on a twin network model and a KNN (K-nearest neighbor)algorithm. The speaker recognition method based on the twin network model and the KNNalgorithmcomprises the steps that S1, voice information of a speaker is collectedby using a microphone and taken as a data set to train an RNN network model; and S2, the speaker is identified by using atrained RNN network model to construct the twin network model and combining the KNN algorithm. By adopting the technical scheme of the speaker recognition method based on the twin network model and the KNNalgorithm, the data set of the speaker in a database is trained, it is ensured that input of each speech signal inputted into a twinnetwork can output the characteristics representing the speaker, distances between different output characteristic vectors are calculated by cosine distance and used in the KNN algorithm for determining whether the speech signals belong to the same speaker or not, the speaker can be identified with asmall amount of samples, the network does not need to be retrained as the number of speakers increases, the requirement of data sample sizeofa neural network isreduced, and meanwhileinstantaneity and accuracy of speaker recognition are effectively improved.

Description

technical field [0001] The invention belongs to the technical field of human-computer interaction, in particular to the technical field of speaker recognition, and specifically designs a speaker recognition method based on a twin network model and a KNN algorithm. Background technique [0002] In the field of human-computer interaction, with the rapid development of artificial intelligence, pattern recognition and other technologies, the interaction between humans and computers is getting closer and closer. The traditional contact interaction methods can no longer meet people's needs. The interactive way of communication habits has become a research hotspot in recent years. As one of the main channels of human-computer interaction, speaker recognition has gradually become an important research topic in the field of interaction. [0003] Existing methods for speaker recognition mainly include methods of speech feature extraction and template matching technology, methods of s...

AI Technical Summary

Problems solved by technology

The method based on template matching is to pre-train the recognized voiceprint samples and match the voiceprint to be recognized with it. This method is simple to operate, but the recognition accuracy is not high and a large number of data samples are required.

In the method based on the speech statistical model, the recognition task is defined as the probability of calculating the variable. This method has high recognition accuracy, but requires a large amount of data for verification.

In the method based on deep learning technology, the neural network is used to capture the hidden features of the speaker so that it can better represent the speaker. This method not only requires a large amount of data, but also requires the neural network to be updated every time the data set is updated. Retraining is not conducive to new information entry

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