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28
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.
Feature Warping for Robust Speaker Verification
- ISCA ARCHIVE
, 2001
"... We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, nonlinear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by cond ..."
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Cited by 191 (10 self)
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We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, nonlinear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by conditioning the variance of the distribution, but not to the extent of conforming the speech statistics to a target distribution. The proposed target mapping method warps the distribution of a cepstral feature stream to a standardised distribution over a specified time interval. We evaluate a number of the enhancement methods for speaker verification, and compare them against a Gaussian target mapping implementation. Results indicate improvements of the warping technique over a number of methods such as Cepstral Mean Subtraction (CMS), modulation spectrum processing, and short-term windowed CMS and variance normalisation. This technique is a suitable feature post-processing method that may be combined with other techniques to enhance speaker recognition robustness under adverse conditions.
Phonetic speaker recognition with support vector machines
- in Advances in Neural Information Processing Systems
"... A recent area of significant progress in speaker recognition is the use of high level features—idiolect, phonetic relations, prosody, discourse structure, etc. A speaker not only has a distinctive acoustic sound but uses language in a characteristic manner. Large corpora of speech data available in ..."
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Cited by 47 (6 self)
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A recent area of significant progress in speaker recognition is the use of high level features—idiolect, phonetic relations, prosody, discourse structure, etc. A speaker not only has a distinctive acoustic sound but uses language in a characteristic manner. Large corpora of speech data available in recent years allow experimentation with long term statistics of phone patterns, word patterns, etc. of an individual. We propose the use of support vector machines and term frequency analysis of phone sequences to model a given speaker. To this end, we explore techniques for text categorization applied to the problem. We derive a new kernel based upon a linearization of likelihood ratio scoring. We introduce a new phone-based SVM speaker recognition approach that halves the error rate of conventional phone-based approaches. 1
The NIST 1999 Speaker Recognition Evaluation - An Overview
, 2000
"... This article summarizes the 1999 NIST Speaker Recognition Evaluation. It discusses the overall research objectives, the three task definitions, the development and evaluation data sets, the specified performance measures and their manner of presentation, the overall quality of the results. More than ..."
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Cited by 45 (4 self)
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This article summarizes the 1999 NIST Speaker Recognition Evaluation. It discusses the overall research objectives, the three task definitions, the development and evaluation data sets, the specified performance measures and their manner of presentation, the overall quality of the results. More than a dozen sites from the United States, Europe, and Asia participated in this evaluation. There were three primary tasks for which automatic systems could be designed: one-speaker detection, twospeaker detection, and speaker tracking. All three tasks were performed in the context of mu-law encoded conversational telephone speech. The one-speaker detection task used single channel data, while the other two tasks used summed two-channel data. About 500 target speakers were specified, with two minutes of training speech data provided for each. Both multiple and single speaker test segments were selected from about 2000 conversations that were not used for training material. The duration of the multiple speaker test data was nominally one minute, while the duration of the single speaker test segments varied from near zero up to sixty seconds. For each task, systems had to make independent decisions for selected combinations of a test segment and a hypothesized target speaker. The data sets for each task were designed to be large enough to provide statistically meaningful results on test subsets of interest. Results were analyzed with respect to various conditions including, duration, pitch differences, and handset types. Keywords speaker recognition, speaker verification, speaker detection, speaker tracking, DET Curve, NIST evaluation 3 1
Robust speaker recognition in noisy conditions
- IEEE TRANS. AUDIO, SPEECH LANG. PROCESS
, 2007
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Spectral Features for Automatic Text-Independent Speaker Recognition
, 2003
"... Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but e#ective representation that is more stable and discriminative than the original signal. Since the front-end is the first component ..."
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Cited by 22 (2 self)
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Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but e#ective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components (speaker modeling and pattern matching) is strongly determined by the quality of the front-end. In other words, classification can be at most as accurate as the features.
Stochastic Feature Transformation with Divergence-Based Out-of-Handset Rejection for Robust Speaker Verification
- EURASIP J. on Applied Signal Processing
, 2004
"... The performance of telephone-based speaker verification systems can be severely degraded by linear and non-linear acoustic distortion caused by telephone handsets. This paper proposes to combine a handset selector with stochastic feature transformation to reduce the distortion. Specifically, a GMMba ..."
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Cited by 11 (6 self)
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The performance of telephone-based speaker verification systems can be severely degraded by linear and non-linear acoustic distortion caused by telephone handsets. This paper proposes to combine a handset selector with stochastic feature transformation to reduce the distortion. Specifically, a GMMbased handset selector is trained to identify the most likely handset used by the claimants, and then handset-specific stochastic feature transformations are applied to the distorted feature vectors. This paper also proposes a divergence-based handset selector with out-of-handset (OOH) rejection capability to identify the `unseen' handsets. This is achieved by measuring the Jensen di#erence between the selector's output and a constant vector with identical elements. The resulting handset selector is combined with the proposed feature transformation technique for telephone-based speaker verification. Experimental results based on 150 speakers of the HTIMIT corpus show that the handset selector, either with or without OOH rejection capability, is able to identify the `seen' handsets accurately (98.3% in both cases). Results also demonstrate that feature transformation performs significantly better than the classical cepstral mean normalization approach. Finally, by using the transformation parameters of the `seen' handsets to transform the utterances with correctly identified handsets and processing those utterances with `unseen' handsets by cepstral mean subtraction, verification error rates are reduced significantly (from 12.41% to 6.59% on average).
Combining Stochastic Feature Transformation And Handset Identification For Telephone-Based Speaker Verification
- In: Proc. ICASSP’02
, 2002
"... The performance of telephone-based speaker verification systems can be severely degraded by the acoustic mismatch caused by telephone handsets. This paper proposes to combine a handset selector with stochastic feature transformation to reduce the mismatch. Specifically, a GMM-based handset selector ..."
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Cited by 10 (7 self)
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The performance of telephone-based speaker verification systems can be severely degraded by the acoustic mismatch caused by telephone handsets. This paper proposes to combine a handset selector with stochastic feature transformation to reduce the mismatch. Specifically, a GMM-based handset selector is trained to identify the most likely handset used by the claimants, and then handset-specific stochastic feature transformations are applied to the distorted feature vectors. To overcome the non-linear distortion introduced by telephone handsets, a 2nd-order stochastic feature transformation is proposed. Estimation algorithms based on the stochastic matching technique and the EM algorithm are derived. Experimental results based on 150 speakers of the HTIMIT corpus show that the handset selector is able to identify the handsets accurately (98.3%), and that both linear and non-linear transformation reduce the error rate significantly (from 12.37% to 5.49%). 1.
Applying Articulatory Features To Telephone-Based Speaker Verification
- in Proc. IEEE International Conference on Acoustic, Speech, and Signal Processing
, 2004
"... This paper presents an approach that uses articulatory features (AFs) derived from spectral features for telephone-based speaker verification. To minimize the acoustic mismatch caused by different handsets, handset-specific normalization is applied to the spectral features before the AFs are extrac ..."
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Cited by 8 (4 self)
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This paper presents an approach that uses articulatory features (AFs) derived from spectral features for telephone-based speaker verification. To minimize the acoustic mismatch caused by different handsets, handset-specific normalization is applied to the spectral features before the AFs are extracted. Experimental results based on 150 speakers using 10 different handsets show that AFs contain useful speaker-specific information for speaker verification and the use of handset-specific normalization significantly lowers the error rates under the handset mismatched conditions. Results also demonstrate that fusing the scores obtained from an AF-based system with those obtained from a spectral feature-based (MFCC) system helps lower the error rates of the individual systems.
NIST Speaker Recognition Evaluation Chronicles
- Proc. Odyssey 2004, The Speaker and Language Recognition Workshop
, 2004
"... NIST has coordinated annual evaluations of textindependent speaker recognition since 1996. During the course of this series of evaluations there have been notable milestones related to the development of the evaluation paradigm and the performance achievements of state-of-the-art systems. We do ..."
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Cited by 1 (0 self)
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NIST has coordinated annual evaluations of textindependent speaker recognition since 1996. During the course of this series of evaluations there have been notable milestones related to the development of the evaluation paradigm and the performance achievements of state-of-the-art systems. We document here the variants of the speaker detection task that have been included in the evaluations and the history of the best performance results for this task.
