Biometric Recognition Using Deep Learning: A Survey <s> Voice Recognition <s> We address the closed-set problem of speaker identification by presenting a novel sparse representation classification algorithm. We propose to develop an over complete dictionary using the GMM mean super vector kernel for all the training utterances. A given test utterance corresponds to only a small fraction of the whole training database. We therefore propose to represent a given test utterance as a linear combination of all the training utterances, thereby generating a naturally sparse representation. Using this sparsity, the unknown vector of coefficients is computed via l1minimization which is also the sparsest solution [12]. Ideally, the vector of coefficients so obtained has nonzero entries representing the class index of the given test utterance. Experiments have been conducted on the standard TIMIT [14] database and a comparison with the state-of-art speaker identification algorithms yields a favorable performance index for the proposed algorithm. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Voice Recognition <s> Speaker diarization is an important front-end for many speech technologies in the presence of multiple speakers, but current methods that employ i-vector clustering for short segments of speech are potentially too cumbersome and costly for the front-end role. In this work, we propose an alternative approach for learning representations via deep neural networks to remove the i-vector extraction process from the pipeline entirely. The proposed architecture simultaneously learns a fixed-dimensional embedding for acoustic segments of variable length and a scoring function for measuring the likelihood that the segments originated from the same or different speakers. Through tests on the CALLHOME conversational telephone speech corpus, we demonstrate that, in addition to streamlining the diarization architecture, the proposed system matches or exceeds the performance of state-of-the-art baselines. We also show that, though this approach does not respond as well to unsupervised calibration strategies as previous systems, the incorporation of well-founded speaker priors sufficiently mitigates this shortcoming. <s> BIB002
Voice Recognition (also known as speaker recognition) is the task of determining a person's ID using the characteristics of one's voice. In a way, speaker recognition includes both behavioral and physiological features, such as accent and pitch respectively. Using automatic ways to perform speaker recognition dates back to 1960s when Bell Laboratories were approached by law enforcement agencies about the possibility of identifying callers who had made verbal bomb threats over the telephone  . Over the years, researchers have developed many models that can perform this task effectively, especially with the help of deep learning. In addition to security applications, it is also being used in virtual personal assistants, such as Google Assistant, so they can recognize and distinguish the phone owner's voice from the others  . Speaker recognition can be classified into speaker identification and speaker verification. speaker identification is the process of determining a person's ID from a set of registered voice using a given utterance BIB001 , whereas speaker verification is the process of accepting or rejecting a proposed identity claimed for a speaker  . Since these two tasks usually share the same evaluation process under commonly-used metrics, the terms are sometimes used interchangeably in referenced papers. Speaker recognition is also closely related to speaker diarization, where an input audio stream is partitioned into homogeneous segments according to the speaker identity BIB002 .