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Speaker Authentication

Part of the Signals and Communication Technology Book Series

DOI: 10.1007/978-3-642-23731-7

ISBN: 978-3-642-23730-0

Publisher: Springer Berlin Heidelberg - Springer Journals

Sep 30, 2011

1 - 10 of 15 Chapters

LitStream

Chapter 1Introduction

Li, Qi (Peter)

Pages: 1 –21

[Authentication is the process of positively verifying the identity of a user, device, or any entity in a computer system, often as a prerequisite to allowing access to resources in the system. Authentication has been used by human for thousands of years to recognize each other, to identify...
LitStream

Chapter 2Multivariate Statistical Analysis and One-Pass Vector Quantization

Li, Qi (Peter)

Pages: 23 –41

[Current speaker authentication algorithms are largely based on multivariate statistical theory. In this chapter, we introduce the most important technical components and concepts of multivariate analysis as they apply to speaker authentication: the multivariate Gaussian (also called normal)...
LitStream

Chapter 3Principal Feature Networks for Pattern Recognition

Li, Qi (Peter)

Pages: 43 –60

[Pattern recognition is one of the fundamental technologies in speaker authentication. Understanding the concept of pattern recognition is important in developing speaker authentication algorithms and applications. There are already many books and tutorial papers on pattern recognition and neural...
LitStream

Chapter 4Non-Stationary Pattern Recognition

Li, Qi (Peter)

Pages: 61 –73

[So far, we have discussed pattern recognition for stationary signals. In this chapter, we will discuss pattern recognition for both stationary and nonstationary signals. In speaker authentication, some tasks, such as speaker identification, are treated as stationary pattern recognition while...
LitStream

Chapter 5Robust Endpoint Detection

Li, Qi (Peter)

Pages: 75 –91

[Often the first step in speech signal processing is the use of endpoint detection to separate speech and silence signals for further processing. This topic has been studied for several decades; however, as wireless communications and VoIP phones are becoming more and more popular, more...
LitStream

Chapter 6Detection-Based Decoder

Li, Qi (Peter)

Pages: 93 –110

[Decoding or searching is an important task in both speaker and speech recognition. In speaker verification (SV), given a spoken password and a speakerdependent hidden Markov model (HMM), the task of decoding or searching is to find optimal state alignments in the sense of maximum likelihood...
LitStream

Chapter 7Auditory-Based Time Frequency Transform

Li, Qi (Peter)

Pages: 111 –134

[Time-frequency transforms play an important role in signal processing. Many speech processing algorithms needs to convert the time domain speech signal 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...
LitStream

Chapter 8Auditory-Based Feature Extraction and Robust Speaker Identification

Li, Qi (Peter)

Pages: 135 –150

[In the previous chapter, we introduced a robust auditory transform (AT). In this chapter, we present an auditory-based feature extraction algorithm based on the AT and apply it to robust speaker identification. Usually, the performances of acoustic models trained in clean speech drop...
LitStream

Chapter 9Fixed-Phrase Speaker Verification

Li, Qi (Peter)

Pages: 151 –156

[As we introduced in Chapter 1, speaker recognition includes speaker identification and speaker verification. In the previous chapters, we discussed speaker identification. In this chapter, we will introduce speaker verification. From an application point of view, speaker verification has more...
LitStream

Chapter 10Robust Speaker Verification with Stochastic Matching

Li, Qi (Peter)

Pages: 157 –164

[In today’s telecommunications environment, which includes wireless, landline, VoIP, and computer networks, the mismatch between training and testing environments poses a big challenge to speaker authentication systems. In Chapter 8, we addressed the mismatch problem from a feature extraction...

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