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"This book examines use of the voice as a biometric measure for personal authentication, offering an overview of advances in speaker authentication, and including useful algorithms and techniques for improving overall system robustness and performance."

"ABSTRACT

This study describes bispectrum pattern analysis and quantization for identifying speaker in noisy environment. Direct, non-parametric, bispectrum analysis and estimation was performed before quantization and classification process. As for reliable quantization approach this study applied an algorithm of vector quantization method using combined Self Organizing Feature Map (SOFM) and Learning Vector Quantization (LVQ) neural network, to quantize bispectrum of speech data. Since there is no prior knowledge on bispectrum data distribution to determine class information, we used an adaptive codebook generation method, which is a hybrid of SOFM to generate the codebook internally and LVQ algorithm to improve the cluster distribution in the classifier decision. In addition with the SOFM+LVQ algorithm, a nonlinear vector quantization method (NLVQ) is introduced in dealing with a case where there is a low-separability problem of codebook data obtained from one speaker. This new NLVQ technique employs a nonlinear third order hyperbolic tangent function which combines noise suppression effect with a dynamic range limitation, in or-der to transform the bispectrum input data to be used for making the codebook. Nearest-neighbor rule statistical analysis was used to estimate the recognition performance of the system before classification."

"Extraction and representation of prosodic features for speech processing applications deals with prosody from speech processing point of view with topics including, the significance of prosody for speech processing applications, why prosody need to be incorporated in speech processing applications, and different methods for extraction and representation of prosody for applications such as speech synthesis, speaker recognition, language recognition and speech recognition."

"Forensic speaker recognition : law enforcement and counter-terrorism is an anthology of the research findings of 35 speaker recognition experts from around the world. The volume provides a multidimensional view of the complex science involved in determining whether a suspect’s voice truly matches forensic speech samples, collected by law enforcement and counter-terrorism agencies, that are associated with the commission of a terrorist act or other crimes. While addressing such topics as the challenges of forensic case work, handling speech signal degradation, analyzing features of speaker recognition to optimize voice verification system performance, and designing voice applications that meet the practical needs of law enforcement and counter-terrorism agencies, this material all sounds a common theme: how the rigors of forensic utility are demanding new levels of excellence in all aspects of speaker recognition. "

"Dialect accent features for establishing speaker identity : a case study discusses the subject of forensic voice identification and speaker profiling. Specifically focusing on speaker profiling and using dialects of the Hindi language, widely used in India, the authors have contributed to the body of research on speaker identification by using accent feature as the discriminating factor. This case study contributes to the understanding of the speaker identification process in a situation where unknown speech samples are in different language/dialect than the recording of a suspect. The authors' data establishes that vowel quality, quantity, intonation and tone of a speaker as compared to Khariboli (standard Hindi) could be the potential features for identification of dialect accent."