Speaker Recognition by Extraction of Audio Signal Parameters
ABSTRACT
Speaker recognition is a challenging task in the field of science and technology. There are various methods for recognition of a speaker. Some of the researchers were used the signal processing technique using embedded systems and some are using programming algorithms to accomplish this task. Digital signal processing is also an efficient tool for this task. Here we are using an artificial intelligent (AI) based technique to complete this task. Here we are trying to differentiate the sound of APJ Abdul Kalam and the Donald Trumph president of America. Herewe are using K-nearest neighbor (KNN) algorithm which is a powerful supervised learning tool. We have extract the five parameters of sound and these are ‘chroma_stft’, ‘chroma_cqt’, ‘zero_crossing_rate’ and ‘mfcc’.
INTRODUCTION
Speaker recognition is related to speaker identification and speaker verification problems. Speaker identification is a taskto identify the provided speech sample (utterance) as belonging to one speaker from the set of known speakers (1:Nmatch). Comparison between people who speak their own words for accepting or rejecting the ratio (1: 1). The gift of prayer, can be a talk, or that may be. You do not know that those who take the language itself and encoding 1, between someone who speaks a word against the tongue, is not the same or any statement from the signification of , says whosoever. In cases where the right is not very well known. With the development of the Emerald Bio and one of the most important aspects of the doctrine of the evidence system, this is more and more information. Many speakers download features like Mel Frequency cepstral coefficients (MFCC) and the collection of background data created by imitation Universal Background Model (UBM) and Gaussian Mixed Model (GMM)) describes the meaning of human language. Linear discriminate analysis (LDA) is often used for feature variance between the most and least feature vectors. Increasing the neural network (DNN) feature is the only Bottle Neck Features (BNF) With DNN large part of the training being divided into the business and going to step into the third, as a passenger. This feature is used as a vector standard for the description of all speakers. All users are the same developer.Modern design often uses a combination of features such as the MFCC feature changed from the source unit and the BNFStill LDA. The results of the room model of the demise or the equivalent of a heavyweight contest details.Consult with security hardware Capacity to limit the speed of uiriumlet the drawing not be used type. The truth is simple to use it also allows multiple algorithms to slow post and when he prayed and the signTemplate. Automatic speaker recognition (ASR)Model was introduced in recent years to work in a binary, However, two unique MFCC The function of some of the features that are appropriate for a feeling;Industry.
Conclusion
Speaker recognition is a challenging task in the field of science and technology. There are various methods for recognition of a speaker. Some of the researchers were used the signal processing technique using embedded systems and some are using programming algorithms to accomplish this task. Digital signal processing is also an efficient tool for this task. Here we are using an artificial intelligent (AI) based technique to complete this task. Here we are trying to differentiate the sound of APJ Abdul Kalam sir and the Donald Trumph president of America. Here we are using K-nearest neighbor (KNN) algorithm which is a powerful supervised learning tool.The model is trained by KNN algorithm and taking the 3 nearest neighbor. The accuracy of the system is 94% and the value of K score is 98. This showing the good estimation of detected signal.
