Speaker verification

Jiang, H. and Deng, L. A Bayesian approach to the verification problem applications to speaker verification. IEEE Transactions on Speech and Audio Processing 9(8) (2001), 874—884. 

Reddy, N. P., and Buch, O. (2000). Committee Neural N otks for Speaker Verification, Intelligent Engineering Systems through Artificial Neural Networks, Vol. 5, Fuzzy Logic and Evolutionary Programming (editors C. Dagli, A. Akay, C. Philips, B. Femadez, J. Ghosh), ASME Press, New York, 911-915. 

Clemins, PJ. and Johnson, M.T. (2002) Automatic type classification and speaker verification of African elephant vocalizations. Animal Behaviour 2002, Vrije Universiteit, Amsterdam. 

Speaker Verification Using Gaussian Mixture Model (GMM)... 

Keywords— Speaker Verification, Gaussian Mixture Model, Equal Error Rate. 

Speaker Recognition can be divided into two parts. Automatic Speaker Identification (ASI) and Automatic Speaker Verification (ASV). Automatic Speaker identification (ASI) is a process used to determine the identity of the test speaker from the registered speaker using specific information retained in the speech signals. This approach will identify the person accessing the system by comparing the vector profile of the test speaker with each of the vector profiles of the speakers that make up the reference set. 

Establishing whether a speaker is claimed correctly by an automatic means from the acoustics of his/her voice is termed as ASV. The operation of most speaker verification (SV) systems can be divided into two phases. The two phases are training phase and verification phase. In this paper the main focus is based on SV. 

Vector Quantization (VQ) technique is used in speaker recognition system. This technique is easy to implement through the use of the Euclidean distance measure. However, it provides less accuracy in terms of performance if only this algorithm was used for the speaker verification system. This algorithm for VQ is base on [3] with large data training required. They used 100 speakers and able to achieve 56% performance improvement using text dependent mode compared to independent. 

Douglas A.Reynolds, Thomas F. Quatieri and Robest B. Dunn (2000 January/April/July). Speaker Verification Using Adapted Gaussian Mixture Models. Pages 19-41. 

Cheang S Y and Ahmad A M (2008) Malay language text-independent speaker verification using NN-MLP classifia- with MFCC. International conference on electronic design, 2008, pp 1-5... 

A fact is an accepted truth whose verification is not affected by its source. No matter who presents it, a fact remains true. We accept some statements as facts because we can test them personally (fire is hot) or because they have been verified frequently by others (penguins live in Antarctica). We accept as fact, for example, that Nicole Brown Simpson and Ronald Goldman were murdered in Los Angeles in 1994. However, the identity of the assassin remains, for some people, a matter of opinion. That is, depending on the speaker, the killer(s) could be an ex-husband, a burglar, a drug dealer, a member of organized crime, or someone else. As you think about your evidence, be careful that you don t present your opinions as facts accepted by everyone. Opinions are debatable, and therefore you must always support them before your readers will be convinced. 

Chinese clinicians have the same competencies as any elsewhere in the world, but possibly few have any experience of working to GCP standards, and the administrators of hospitals are also not familiar with the concept. Thus, much time and energy needs to be directed at the training of investigators and those with power to sell the concept of source data verification, and such a task must be done by a Chinese speaker because of the subtlety of the alphabet and the risk of misunderstanding. 

While we can find the optimal parameters for any given frame for a model such as LF, we never actually achieve a perfect fit. In fact, we can determine a sample by sample difference between the model and the residual. This of course, is another residual or error signal, which now represents the modelling error after the LP model and the LF model have been taken into account. One interesting study by Plumpe et al [356] describes how this signal is in fact highly individual to speakers and can be used as the basis of speaker recognition or verification. 

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