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47
Speaker verification using Adapted Gaussian mixture models
- Digital Signal Processing
, 2000
"... In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but ef ..."
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Cited by 1010 (42 self)
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In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance is also described and discussed. Finally, representative performance benchmarks and system behavior experiments on NIST SRE corpora are presented. © 2000 Academic Press Key Words: speaker recognition; Gaussian mixture models; likelihood ratio detector; universal background model; handset normalization; NIST evaluation. 1.
A Tutorial on Text-Independent Speaker Verification
- EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2004:4, 430–451
, 2004
"... This paper presents an overview of a state-of-the-art text-independent speaker verification system. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, ..."
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Cited by 138 (13 self)
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This paper presents an overview of a state-of-the-art text-independent speaker verification system. First, an introduction proposes a modular scheme of the training and test phases of a speaker verification system. Then, the most commonly speech parameterization used in speaker verification, namely, cepstral analysis, is detailed. Gaussian mixture modeling, which is the speaker modeling technique used in most systems, is then explained. A few speaker modeling alternatives, namely, neural networks and support vector machines, are mentioned. Normalization of scores is then explained, as this is a very important step to deal with real-world data. The evaluation of a speaker verification system is then detailed, and the detection error trade-off (DET) curve is explained. Several extensions of speaker verification are then enumerated, including speaker tracking and segmentation by speakers. Then, some applications of speaker verification are proposed, including on-site applications, remote applications, applications relative to structuring audio information, and games. Issues concerning the forensic area are then recalled, as we believe it is very important to inform people about the actual performance and limitations of speaker verification systems. This paper concludes by giving a
Automatic Person Verification Using Speech and Face Information
, 2003
"... Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one pre ..."
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Cited by 37 (7 self)
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Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person's speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems
Handset-Dependent Background Models For Robust Text-Independent Speaker Recognition
- In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing
, 1997
"... This paper studies the effects of handset distortion on telephone -based speaker recognition performance, resulting in the following observations: (1) the major factor in speaker recognition errors is whether the handset type (e.g., electret, carbon) is different across training and testing, not whe ..."
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Cited by 37 (8 self)
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This paper studies the effects of handset distortion on telephone -based speaker recognition performance, resulting in the following observations: (1) the major factor in speaker recognition errors is whether the handset type (e.g., electret, carbon) is different across training and testing, not whether the telephone lines are mismatched, (2) the distribution of speaker recognition scores for true speakers is bimodal, with one mode dominated by matched handset tests and the other by mismatched handsets, (3) cohort-based normalization methods derive much of their performance gains from implicitly selecting cohorts trained with the same handset type as the claimant, and (4) utilizing a handset-dependent background model which is matched to the handset type of the claimant's training data sharpens and separates the true and false speaker score distributions. Results on the 1996 NIST Speaker Recognition Evaluation corpus show that using handset-matched background models reduces false accep...
Activity-aware ECG-based patient authentication for remote health monitoring
"... Mobile medical sensors promise to provide an efficient, accurate, and economic way to monitor patients ’ health outside the hospital. Patient authentication is a necessary security requirement in remote health monitoring scenarios. The monitoring system needs to make sure that the data is coming fro ..."
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Cited by 16 (6 self)
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Mobile medical sensors promise to provide an efficient, accurate, and economic way to monitor patients ’ health outside the hospital. Patient authentication is a necessary security requirement in remote health monitoring scenarios. The monitoring system needs to make sure that the data is coming from the right person before any medical or financial decisions are made based on the data. Credentialbased authentication methods (e.g., passwords, certificates) are not well-suited for remote healthcare as patients could hand over credentials to someone else. Furthermore, one-time authentication using credentials or trait-based biometrics (e.g., face, fingerprints, iris) do not cover the entire monitoring period and may lead to unauthorized post-authentication use. Recent studies have shown that the human electrocardiogram (ECG) exhibits unique patterns that can be used to discriminate individuals. However, perturbation of the ECG signal due to physical activity is a major obstacle in applying the technology in real-world situations. In this paper, we present a novel ECG and accelerometer-based system that can authenticate individuals in an ongoing manner under various activity conditions. We describe the probabilistic authentication system we have developed and present experimental results from 17 individuals. 1.
Speaker recognition with polynomial classifiers
- IEEE Transactions on Speech and Audio Processing
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Speaker verification using minimum verification error training
- In Proc. ofICASSP '98
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Review of classifier combination methods
- In Machine Learning in Document Analysis and Recognition. Informatica 34 (2010) 111–118 S. Vemulapalli et al
, 2008
"... Summary. Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combin ..."
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Cited by 12 (2 self)
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Summary. Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still missing and achieved improvements are inconsistent. By introducing different categories of classifier combinations in this review we attempt to put forward more specific directions for future theoretical research. We also introduce a retraining effect and effects of locality based training as important properties of classifier combinations. Such effects have significant influence on the performance of combinations, and their study is necessary for complete theoretical understanding of combination algorithms. 1
Likelihood Normalization For Face Authentication In Variable Recording Conditions
, 2002
"... In this paper we evaluate the effectiveness of two likelihood normalization techniques, the Background Model Set (BMS) and the Universal Background Model (UBM), for improving performance and robustness of four face authentication systems utilizing a Gaussian Mixture Model (GMM) classifier. The syste ..."
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Cited by 9 (3 self)
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In this paper we evaluate the effectiveness of two likelihood normalization techniques, the Background Model Set (BMS) and the Universal Background Model (UBM), for improving performance and robustness of four face authentication systems utilizing a Gaussian Mixture Model (GMM) classifier. The systems differ in the feature extraction method used: eigenfaces (PCA), 2-D DCT, 2-D Gabor wavelets and DCT-mod2. Experiments on the VidTIMIT database, using test images corrupted either by an illumination change or compression artefacts, suggest that likelihood normalization has little effect when using PCA derived features, while providing significant performance improvements when using the remaining features.
General Phrase Speaker Verification Using Sub-Word Background Models And Likelihood-Ratio Scoring
- In Proc. Int. Conf. on Spoken Language Processing
, 1996
"... We present a design and study the performance of a text-dependent speaker verification system using general phrase passwords. The text of the password utterance and its phone transcription are assumed to be available. The problems that are addressed include the appropriate choice of units for buildi ..."
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Cited by 9 (0 self)
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We present a design and study the performance of a text-dependent speaker verification system using general phrase passwords. The text of the password utterance and its phone transcription are assumed to be available. The problems that are addressed include the appropriate choice of units for building target speaker models and the choice of background models for likelihood ratio scoring.
