Titlebook: Speaker Authentication; Qi (Peter) Li Book 2012 Springer-Verlag Berlin Heidelberg 2012 Indentification.Speaker Autentification.Speech Reco

慢跑鞋

innate

Fast Discriminative Training, real applications, to define an objective and derive an estimation algorithm is a joint design process. This chapter presents an example where a discriminative objective was defined together with its fast training algorithm.

枯萎将要

Introduction,llowing access to resources in the system. Authentication has been used by human for thousands of years to recognize each other, to identify friends and enemies, and to protect their information and assets. In the computer era, the purpose of identification is more than just to identify people in ou

neoplasm

Multivariate Statistical Analysis and One-Pass Vector Quantization,ical components and concepts of multivariate analysis as they apply to speaker authentication: the multivariate Gaussian (also called normal) distribution, principal component analysis (PCA), vector quantization (VQ), and segmental K-means. These fundamental techniques have been used for statistical

misanthrope

Principal Feature Networks for Pattern Recognition, developing speaker authentication algorithms and applications. There are already many books and tutorial papers on pattern recognition and neural network. Instead of repeating a similar introduction of the fundamental pattern recognition and neural networks techniques, we introduce a different appr

宣誓书

Non-Stationary Pattern Recognition,stationary signals. In speaker authentication, some tasks, such as speaker identification, are treated as stationary pattern recognition while others, such as speaker verification, are treated as non-stationary pattern recognition. We will introduce the stochastic modeling approach for both stationa

拖网

GRATE

过于光泽

Auditory-Based Time Frequency Transform,l to a frequency domain. The Fourier transform (FT) and the fast Fourier transform (FFT) have been used for decades, but they are not robust to background noise. As shown in this chapter, FFT generates computation noise and pitch harmonics during its computation. In a different approach, the traveli

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Auditory-Based Feature Extraction and Robust Speaker Identification,sed on the AT and apply it to robust speaker identification. Usually, the performances of acoustic models trained in clean speech drop significantly when tested in noisy speech. The presented features, however, have shown strong robustness in this kind of situation. We present a typical text-indepen