The Experts below are selected from a list of 18195 Experts worldwide ranked by ideXlab platform
Joseph P. Campbell - One of the best experts on this subject based on the ideXlab platform.
-
Phonetic Speaker Recognition
Conference Record of Thirty-Fifth Asilomar Conference on Signals Systems and Computers (Cat.No.01CH37256), 2020Co-Authors: Mary A. Kohler, Joseph P. Campbell, Walter Andrews, J. Herndndez-corderoAbstract:This paper introduces a novel language-independent Speaker-Recognition system based on differences in dynamic realization of phonetic features (i.e., pronunciation) between Speakers rather than spectral differences in voice quality. The system exploits phonetic information from six languages to perform text independent Speaker Recognition. All experiments were performed on the NIST 2001 Speaker Recognition Evaluation Extended Data Task. Recognition results are provided for unigram, bigram, and trigram models. Performance for each of the three models is examined for phones from each individual language and the final multilanguage fused system. Additional fusion experiments demonstrate that Speaker Recognition capability is maintained even without phonetic information in the language of the Speaker.
-
Forensic Speaker Recognition
IEEE Signal Processing Magazine, 2009Co-Authors: Joseph P. Campbell, Reva Schwartz, Wade Shen, William M. Campbell, Jean-françois Bonastre, Driss MatroufAbstract:Looking at the different points highlighted in this article, we affirm that forensic applications of Speaker Recognition should still be taken under a necessary need for caution. Disseminating this message remains one of the most important responsibilities of Speaker Recognition researchers.
-
gender dependent phonetic refraction for Speaker Recognition
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Walter Andrews, Joseph P. Campbell, Mary A. Kohler, John J Godfrey, Jaime HernandezcorderoAbstract:This paper describes improvements to an innovative high-performance Speaker Recognition system. Recent experiments showed that with sufficient training data phone strings from multiple languages are exceptional features for Speaker Recognition. The prototype phonetic Speaker Recognition system used phone sequences from six languages to produce an equal error rate of 11.5% on Switchboard-I audio files. The improved system described in this paper reduces the equal error rate to less then 4%. This is accomplished by incorporating gender-dependent phone models, pre-processing the speech files to remove cross-talk, and developing more sophisticated fusion techniques for the multi-language likelihood scores.
-
ICASSP - Corpora for the evaluation of Speaker Recognition systems
1999 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Co-Authors: Joseph P. Campbell, D.a. ReynoldsAbstract:Using standard speech corpora for development and evaluation has proven to be very valuable in promoting progress in speech and Speaker Recognition research. In this paper, we present an overview of current publicly available corpora intended for Speaker Recognition research and evaluation. We outline the corpora's salient features with respect to their suitability for conducting Speaker Recognition experiments and evaluations. We hope to increase the awareness and use of these standard corpora and corresponding evaluation procedures throughout the Speaker Recognition community.
-
Speaker Recognition : A tutorial : Automated biometrics
1997Co-Authors: Joseph P. CampbellAbstract:A tutorial on the design and development of automatic Speaker-Recognition systems is presented. Automatic Speaker Recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic Speaker-Recognition systems are shown and design tradeoffs are discussed. Then, a new automatic Speaker-Recognition system is given. This recognizer performs with 98.9% correct identification. Last, the performances of various systems are compared.
Sadaoki Furui - One of the best experts on this subject based on the ideXlab platform.
-
Speech and Speaker Recognition Evaluation
Evaluation of Text and Speech Systems, 2007Co-Authors: Sadaoki FuruiAbstract:This chapter overviews techniques for evaluating speech and Speaker Recognition systems. The chapter first describes principles of Recognition methods, and specifies types of systems as well as their applications. The evaluation methods can be classified into subjective and objective methods, among which the chapter focuses on the latter methods. In order to compare/normalize performances of different speech Recognition systems, test set perplexity is introduced as a measure of the difficulty of each task. Objective evaluation methods of spoken dialogue and transcription systems are respectively described. Speaker Recognition can be classified into Speaker identification and verification, and most of the application systems fall into the Speaker verification category. Since variation of speech features over time is a serious problem in Speaker Recognition, normalization and adaptation techniques are also described. Speaker verification performance is typically measured by equal error rate, detection error trade-off (DET) curves, and a weighted cost value. The chapter concludes by summarizing various issues for future research.
-
recent advances in Speaker Recognition
International Conference on Audio- and Video-Based Biometric Person Authentication, 1997Co-Authors: Sadaoki FuruiAbstract:This paper introduces recent advances in Speaker Recognition technology. The first part discusses general topics and issues. The second part is devoted to a discussion of more specific topics of recent interest that have led to interesting new approaches and techniques. They include VQ- and ergodic-HMM-based text-independent Recognition methods, a text-prompted Recognition method, parameter/distance normalization and model adaptation techniques, and methods of updating models and a priori thresholds in Speaker verification. Although many recent advances and successes have been achieved in Speaker Recognition, there are still many problems for which good solutions remain to be found. The last part of this paper describes 16 open questions about Speaker Recognition. The paper concludes with a short discussion assessing the current status and future possibilities.
-
Recent advances in Speaker Recognition
Pattern Recognition Letters, 1997Co-Authors: Sadaoki FuruiAbstract:This paper introduces recent advances in Speaker Recognition technology. The first part discusses general topics and issues. The second part is devoted to a discussion of more specific topics of recent interest that have led to interesting new approaches and techniques. They include VQ-and ergodic-HMM-based text-independent Recognition methods, a text-prompted Recognition method, parameter/distance normalization and model adaptation techniques, and methods of updating models and a priori thresholds in Speaker verification. Although many recent advances and successes have been achieved in Speaker Recognition, there are still many problems for which good solutions remain to be found. The last part of this paper describes 16 open questions about Speaker Recognition. The paper concludes with a short discussion assessing the current status and future possibilities. © 1997 Elsevier Science B.V.
-
Speaker Recognition technology
Ntt Review, 1995Co-Authors: T. Matsui, Sadaoki FuruiAbstract:Speaker Recognition is the process of automatically recognizing who is speaking. This paper introduces three Speaker Recognition methods; a text-dependent method which uses predetermined keywords, a text-independent method which does not rely on a specific text being spoken, and a textprompted method in which the system prompts each user with a new key sentence every time the system is used. Recently, AT & T and TI (with US Sprint) started field tests and real application of Speaker Recognition technology, and the Sprint's Voice Phone Card has already been used by many customers. However, there still remain many technical problems to be solved. Among them, this paper introduces several issues that have recently been investigated at NTT Laboratories
John M. Acken - One of the best experts on this subject based on the ideXlab platform.
-
Effects of equipment variation on Speaker Recognition error rates
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Clark D. Shaver, John M. AckenAbstract:Speaker Recognition is one biometric used in identity authentication. As use of biometric systems become more widespread the need for robustness in various environments and applications becomes more significant. One such variable is recording equipment. Speaker Recognition systems in distributed environments, such as the internet, are likely to use different microphones to perform identity authentication. The success rates for Speaker Recognition systems are dependent upon the variability of the recording system performance in a Speaker Recognition system. This paper investigates how Speaker Recognition false accept, false reject rates are affected by variations in recording systems.
-
ICASSP - Effects of equipment variation on Speaker Recognition error rates
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Clark D. Shaver, John M. AckenAbstract:Speaker Recognition is one biometric used in identity authentication. As use of biometric systems become more widespread the need for robustness in various environments and applications becomes more significant. One such variable is recording equipment. Speaker Recognition systems in distributed environments, such as the internet, are likely to use different microphones to perform identity authentication. The success rates for Speaker Recognition systems are dependent upon the variability of the recording system performance in a Speaker Recognition system. This paper investigates how Speaker Recognition false accept, false reject rates are affected by variations in recording systems.
Driss Matrouf - One of the best experts on this subject based on the ideXlab platform.
-
Forensic Speaker Recognition
IEEE Signal Processing Magazine, 2009Co-Authors: Joseph P. Campbell, Reva Schwartz, Wade Shen, William M. Campbell, Jean-françois Bonastre, Driss MatroufAbstract:Looking at the different points highlighted in this article, we affirm that forensic applications of Speaker Recognition should still be taken under a necessary need for caution. Disseminating this message remains one of the most important responsibilities of Speaker Recognition researchers.
Clark D. Shaver - One of the best experts on this subject based on the ideXlab platform.
-
Effects of equipment variation on Speaker Recognition error rates
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Clark D. Shaver, John M. AckenAbstract:Speaker Recognition is one biometric used in identity authentication. As use of biometric systems become more widespread the need for robustness in various environments and applications becomes more significant. One such variable is recording equipment. Speaker Recognition systems in distributed environments, such as the internet, are likely to use different microphones to perform identity authentication. The success rates for Speaker Recognition systems are dependent upon the variability of the recording system performance in a Speaker Recognition system. This paper investigates how Speaker Recognition false accept, false reject rates are affected by variations in recording systems.
-
ICASSP - Effects of equipment variation on Speaker Recognition error rates
2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Clark D. Shaver, John M. AckenAbstract:Speaker Recognition is one biometric used in identity authentication. As use of biometric systems become more widespread the need for robustness in various environments and applications becomes more significant. One such variable is recording equipment. Speaker Recognition systems in distributed environments, such as the internet, are likely to use different microphones to perform identity authentication. The success rates for Speaker Recognition systems are dependent upon the variability of the recording system performance in a Speaker Recognition system. This paper investigates how Speaker Recognition false accept, false reject rates are affected by variations in recording systems.
