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Graph embedding for speaker recognition

January 1, 2013
  |
Book Chapter
Author:
Zahi N. Karam
…
Published in:
Chapter in Graph Embedding for Pattern Analysis, 2013, pp. 229-60.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

This chapter presents applications of graph embedding to the problem of text-independent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison-given two speech recordings (utterances), produce a score which measures speaker similarity. Using speaker comparison, other applications can be implemented-speaker clustering (grouping similar speakers in a corpus), speaker verification (verifying a claim of identity), speaker identification (identifying a speaker out of a list of potential candidates), and speaker retrieval (finding matches to a query set).
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Summary

This chapter presents applications of graph embedding to the problem of text-independent speaker recognition. Speaker recognition is a general term encompassing multiple applications. At the core is the problem of speaker comparison-given two speech recordings (utterances), produce a score which measures speaker similarity. Using speaker comparison, other applications can be...

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Graph embedding for speaker recognition

Graph relational features for speaker recognition and mining

June 28, 2011
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Conference Paper
Author:
Zahi N. Karam
…
Published in:
Proc. 2011 IEEE Statistical Signal Processing Workshop (SSP), 28-30 June 2011, pp. 525-528.
Topic:
social network
R&D area:
R&D group:

Summary

Recent advances in the field of speaker recognition have resulted in highly efficient speaker comparison algorithms. The advent of these algorithms allows for leveraging a background set, consisting a large numbers of unlabeled recordings, to improve recognition. In this work, a relational graph, where nodes represent utterances and links represent speaker similarity, is created from the background recordings in which the recordings of interest, train and test, are then embedded. Relational features computed from the embedding are then used to obtain a match score between the recordings of interest. We show the efficacy of these features in speaker verification and speaker mining tasks.
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Summary

Recent advances in the field of speaker recognition have resulted in highly efficient speaker comparison algorithms. The advent of these algorithms allows for leveraging a background set, consisting a large numbers of unlabeled recordings, to improve recognition. In this work, a relational graph, where nodes represent utterances and links represent...

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Graph relational features for speaker recognition and mining

The MIT LL 2010 speaker recognition evaluation system: scalable language-independent speaker recognition

May 22, 2011
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Conference Paper
Author:
Douglas E. Sturim
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5272-5275.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Research in the speaker recognition community has continued to address methods of mitigating variational nuisances. Telephone and auxiliary-microphone recorded speech emphasize the need for a robust way of dealing with unwanted variation. The design of recent 2010 NIST-SRE Speaker Recognition Evaluation (SRE) reflects this research emphasis. In this paper, we present the MIT submission applied to the tasks of the 2010 NIST-SRE with two main goals--language-independent scalable modeling and robust nuisance mitigation. For modeling, exclusive use of inner product-based and cepstral systems produced a language-independent computationally-scalable system. For robustness, systems that captured spectral and prosodic information, modeled nuisance subspaces using multiple novel methods, and fused scores of multiple systems were implemented. The performance of the system is presented on a subset of the NIST SRE 2010 core tasks.
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Summary

Research in the speaker recognition community has continued to address methods of mitigating variational nuisances. Telephone and auxiliary-microphone recorded speech emphasize the need for a robust way of dealing with unwanted variation. The design of recent 2010 NIST-SRE Speaker Recognition Evaluation (SRE) reflects this research emphasis. In this paper, we...

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The MIT LL 2010 speaker recognition evaluation system: scalable language-independent speaker recognition

Towards reduced false-alarms using cohorts

May 22, 2011
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Conference Paper
Author:
Zahi N. Karam
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 4512-4515.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

The focus of the 2010 NIST Speaker Recognition Evaluation (SRE) was the low false alarm regime of the detection error trade-off (DET) curve. This paper presents several approaches that specifically target this issue. It begins by highlighting the main problem with operating in the low-false alarm regime. Two sets of methods to tackle this issue are presented that require a large and diverse impostor set: the first set penalizes trials whose enrollment and test utterances are not nearest neighbors of each other while the second takes an adaptive score normalization approach similar to TopNorm and ATNorm.
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Summary

The focus of the 2010 NIST Speaker Recognition Evaluation (SRE) was the low false alarm regime of the detection error trade-off (DET) curve. This paper presents several approaches that specifically target this issue. It begins by highlighting the main problem with operating in the low-false alarm regime. Two sets of...

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Towards reduced false-alarms using cohorts

Graph-embedding for speaker recognition

September 30, 2010
  |
Conference Paper
Author:
Zahi N. Karam
…
Published in:
INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 26-30 September 2010, pp. 2742-2745.
Topic:
social network
R&D area:
R&D group:

Summary

Popular methods for speaker classification perform speaker comparison in a high-dimensional space, however, recent work has shown that most of the speaker variability is captured by a low-dimensional subspace of that space. In this paper we examine whether additional structure in terms of nonlinear manifolds exist within the high-dimensional space. We will use graph embedding as a proxy to the manifold and show the use of the embedding in data visualization and exploration. ISOMAP will be used to explore the existence and dimension of the space. We also examine whether the manifold assumption can help in two classification tasks: data-mining and standard NIST speaker recognition evaluations (SRE). Our results show that the data lives on a manifold and that exploiting this structure can yield significant improvements on the data-mining task. The improvement in preliminary experiments on all trials of the NIST SRE Eval-06 core task are less but significant.
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Summary

Popular methods for speaker classification perform speaker comparison in a high-dimensional space, however, recent work has shown that most of the speaker variability is captured by a low-dimensional subspace of that space. In this paper we examine whether additional structure in terms of nonlinear manifolds exist within the high-dimensional space...

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Graph-embedding for speaker recognition

Simple and efficient speaker comparison using approximate KL divergence

September 26, 2010
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Conference Paper
Author:
William M. Campbell
…
Published in:
INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 26-30 September 2010, pp. 362-365.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

We describe a simple, novel, and efficient system for speaker comparison with two main components. First, the system uses a new approximate KL divergence distance extending earlier GMM parameter vector SVM kernels. The approximate distance incorporates data-dependent mixture weights as well as the standard MAP-adapted GMM mean parameters. Second, the system applies a weighted nuisance projection method for channel compensation. A simple eigenvector method of training is presented. The resulting speaker comparison system is straightforward to implement and is computationally simple? only two low-rank matrix multiplies and an inner product are needed for comparison of two GMM parameter vectors. We demonstrate the approach on a NIST 2008 speaker recognition evaluation task. We provide insight into what methods, parameters, and features are critical for good performance.
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Summary

We describe a simple, novel, and efficient system for speaker comparison with two main components. First, the system uses a new approximate KL divergence distance extending earlier GMM parameter vector SVM kernels. The approximate distance incorporates data-dependent mixture weights as well as the standard MAP-adapted GMM mean parameters. Second, the...

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Simple and efficient speaker comparison using approximate KL divergence

Speaker comparison with inner product discriminant functions

December 7, 2009
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Conference Paper
Author:
William M. Campbell
…
Published in:
Neural Information Processing Symp., 7 December 2009.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Speaker comparison, the process of finding the speaker similarity between two speech signals, occupies a central role in a variety of applications - speaker verification, clustering, and identification. Speaker comparison can be placed in a geometric framework by casting the problem as a model comparison process. For a given speech signal, feature vectors are produced and used to adapt a Gaussian mixture model (GMM). Speaker comparison can then be viewed as the process of compensating and finding metrics on the space of adapted models. We propose a framework, inner product discriminant functions (IPDFs), which extends many common techniques for speaker comparison - support vector machines, joint factor analysis, and linear scoring. The framework uses inner products between the parameter vectors of GMM models motivated by several statistical methods. Compensation of nuisances is performed via linear transforms on GMM parameter vectors. Using the IPDF framework, we show that many current techniques are simple variations of each other. We demonstrate, on a 2006 NIST speaker recognition evaluation task, new scoring methods using IPDFs which produce excellent error rates and require significantly less computation than current techniques.
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Summary

Speaker comparison, the process of finding the speaker similarity between two speech signals, occupies a central role in a variety of applications - speaker verification, clustering, and identification. Speaker comparison can be placed in a geometric framework by casting the problem as a model comparison process. For a given speech...

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Speaker comparison with inner product discriminant functions

A framework for discriminative SVM/GMM systems for language recognition

September 6, 2009
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Conference Paper
Author:
William M. Campbell
…
Published in:
INTERSPEECH 2009, 6-10 September 2009.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Language recognition with support vector machines and shifted-delta cepstral features has been an excellent performer in NIST-sponsored language evaluation for many years. A novel improvement of this method has been the introduction of hybrid SVM/GMM systems. These systems use GMM supervectors as an SVM expansion for classification. In prior work, methods for scoring SVM/GMM systems have been introduced based upon either standard SVM scoring or GMM scoring with a pushed model. Although prior work showed experimentally that GMM scoring yielded better results, no framework was available to explain the connection between SVM scoring and GMM scoring. In this paper, we show that there are interesting connections between SVM scoring and GMM scoring. We provide a framework both theoretically and experimentally that connects the two scoring techniques. This connection should provide the basis for further research in SVM discriminative training for GMM models.
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Summary

Language recognition with support vector machines and shifted-delta cepstral features has been an excellent performer in NIST-sponsored language evaluation for many years. A novel improvement of this method has been the introduction of hybrid SVM/GMM systems. These systems use GMM supervectors as an SVM expansion for classification. In prior work...

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A framework for discriminative SVM/GMM systems for language recognition

The MIT Lincoln Laboratory 2008 speaker recognition system

September 6, 2009
  |
Conference Paper
Author:
Douglas E. Sturim
…
Published in:
INTERSPEECH 2009, 6-10 September 2009.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

In recent years methods for modeling and mitigating variational nuisances have been introduced and refined. A primary emphasis in this years NIST 2008 Speaker Recognition Evaluation (SRE) was to greatly expand the use of auxiliary microphones. This offered the additional channel variations which has been a historical challenge to speaker verification systems. In this paper we present the MIT Lincoln Laboratory Speaker Recognition system applied to the task in the NIST 2008 SRE. Our approach during the evaluation was two-fold: 1) Utilize recent advances in variational nuisance modeling (latent factor analysis and nuisance attribute projection) to allow our spectral speaker verification systems to better compensate for the channel variation introduced, and 2) fuse systems targeting the different linguistic tiers of information, high and low. The performance of the system is presented when applied on a NIST 2008 SRE task. Post evaluation analysis is conducted on the sub-task when interview microphones are present.
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Summary

In recent years methods for modeling and mitigating variational nuisances have been introduced and refined. A primary emphasis in this years NIST 2008 Speaker Recognition Evaluation (SRE) was to greatly expand the use of auxiliary microphones. This offered the additional channel variations which has been a historical challenge to speaker...

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The MIT Lincoln Laboratory 2008 speaker recognition system

Variability compensated support vector machines applied to speaker verification

September 6, 2009
  |
Conference Paper
Author:
Zahi N. Karam
…
Published in:
INTERSPEECH 2009, Proc. of the 10th Annual Conf. of the Internatinoal Speech Communication Association, 6-9 September 2009, pp. 1555-1558.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Speaker verification using SVMs has proven successful, specifically using the GSV Kernel [1] with nuisance attribute projection (NAP) [2]. Also, the recent popularity and success of joint factor analysis [3] has led to promising attempts to use speaker factors directly as SVM features [4]. NAP projection and the use of speaker factors with SVMs are methods of handling variability in SVM speaker verification: NAP by removing undesirable nuisance variability, and using the speaker factors by forcing the discrimination to be performed based on inter-speaker variability. These successes have led us to propose a new method we call variability compensated SVM (VCSVM) to handle both inter and intra-speaker variability directly in the SVM optimization. This is done by adding a regularized penalty to the optimization that biases the normal to the hyperplane to be orthogonal to the nuisance subspace or alternatively to the complement of the subspace containing the inter-speaker variability. This bias will attempt to ensure that inter-speaker variability is used in the recognition while intra-speaker variability is ignored. In this paper we present the theory and promising results on nuisance compensation.
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Summary

Speaker verification using SVMs has proven successful, specifically using the GSV Kernel [1] with nuisance attribute projection (NAP) [2]. Also, the recent popularity and success of joint factor analysis [3] has led to promising attempts to use speaker factors directly as SVM features [4]. NAP projection and the use of...

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Variability compensated support vector machines applied to speaker verification
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