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Forensic speaker recognition: a need for caution

March 1, 2009
  |
Journal Article
Author:
Joseph P. Campbell Jr
…
Published in:
IEEE Signal Process. Mag., Vol. 26, No. 2, March 2009, pp. 95-103.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

There has long been a desire to be able to identify a person on the basis of his or her voice. For many years, judges, lawyers, detectives, and law enforcement agencies have wanted to use forensic voice authentication to investigate a suspect or to confirm a judgment of guilt or innocence. Challenges, realities, and cautions regarding the use of speaker recognition applied to forensic-quality samples are presented.
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Summary

There has long been a desire to be able to identify a person on the basis of his or her voice. For many years, judges, lawyers, detectives, and law enforcement agencies have wanted to use forensic voice authentication to investigate a suspect or to confirm a judgment of guilt or...

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Forensic speaker recognition: a need for caution

Cognitive services for the user

January 1, 2009
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Book Chapter
Author:
Joseph P. Campbell Jr
…
Published in:
Chapter 10, Cognitive Radio Technology, 2009, pp. 305-324.
Topic:
biometrics
R&D area:
R&D group:

Summary

Software-defined cognitive radios (CRs) use voice as a primary input/output (I/O) modality and are expected to have substantial computational resources capable of supporting advanced speech- and audio-processing applications. This chapter extends previous work on speech applications (e.g., [1]) to cognitive services that enhance military mission capability by capitalizing on automatic processes, such as speech information extraction and understanding the environment. Such capabilities go beyond interaction with the intended user of the software-defined radio (SDR) - they extend to speech and audio applications that can be applied to information that has been extracted from voice and acoustic noise gathered from other users and entities in the environment. For example, in a military environment, situational awareness and understanding could be enhanced by informing users based on processing voice and noise from both friendly and hostile forces operating in a given battle space. This chapter provides a survey of a number of speech- and audio-processing technologies and their potential applications to CR, including: - A description of the technology and its current state of practice. - An explanation of how the technology is currently being applied, or could be applied, to CR. - Descriptions and concepts of operations for how the technology can be applied to benefit users of CRs. - A description of relevant future research directions for both the speech and audio technologies and their applications to CR. A pictorial overview of many of the core technologies with some applications presented in the following sections is shown in Figure 10.1. Also shown are some overlapping components between the technologies. For example, Gaussian mixture models (GMMs) and support vector machines (SVMs) are used in both speaker and language recognition technologies [2]. These technologies and components are described in further detail in the following sections. Speech and concierge cognitive services and their corresponding applications are covered in the following sections. The services covered include speaker recognition, language identification (LID), text-to-speech (TTS) conversion, speech-to-text (STT) conversion, machine translation (MT), background noise suppression, speech coding, speaker characterization, noise management, noise characterization, and concierge services. These technologies and their potential applications to CR are discussed at varying levels of detail commensurate with their innovation and utility.
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Summary

Software-defined cognitive radios (CRs) use voice as a primary input/output (I/O) modality and are expected to have substantial computational resources capable of supporting advanced speech- and audio-processing applications. This chapter extends previous work on speech applications (e.g., [1]) to cognitive services that enhance military mission capability by capitalizing on automatic...

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Cognitive services for the user

Gaussian mixture models

January 1, 2009
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Journal Article
Author:
Douglas A. Reynolds
Published in:
Article in Encyclopedia of Biometrics, 2009, pp. 659-63. DOI: https://doi-org.ezproxyberklee.flo.org/10.1007/978-0-387-73003-5_196
Topic:
speaker recognition
R&D area:
R&D group:

Summary

A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model.
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Summary

A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker...

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Gaussian mixture models

Beyond frame independence: parametric modelling of time duration in speaker and language recognition

September 22, 2008
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Conference Paper
Author:
Alan V. McCree
…
Published in:
INTERSPEECH 2008, 22-26 September 2008, pp. 767-770.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

In this work, we address the question of generating accurate likelihood estimates from multi-frame observations in speaker and language recognition. Using a simple theoretical model, we extend the basic assumption of independent frames to include two refinements: a local correlation model across neighboring frames, and a global uncertainty due to train/test channel mismatch. We present an algorithm for discriminative training of the resulting duration model based on logistic regression combined with a bisection search. We show that using this model we can achieve state-of-the-art performance for the NIST LRE07 task. Finally, we show that these more accurate class likelihood estimates can be combined to solve multiple problems using Bayes' rule, so that we can expand our single parametric backend to replace all six separate back-ends used in our NIST LRE submission for both closed and open sets.
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Summary

In this work, we address the question of generating accurate likelihood estimates from multi-frame observations in speaker and language recognition. Using a simple theoretical model, we extend the basic assumption of independent frames to include two refinements: a local correlation model across neighboring frames, and a global uncertainty due to...

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Beyond frame independence: parametric modelling of time duration in speaker and language recognition

Bridging the gap between linguists and technology developers: large-scale, sociolinguistic annotation for dialect and speaker recognition

May 28, 2008
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Conference Paper
Author:
Christopher Cieri
…
Published in:
Proc. 6th Int. Conf. on Language Resources and Evaluation, LREC, 28 May 2008.
Topic:
cyber security
R&D area:
R&D group:

Summary

Recent years have seen increased interest within the speaker recognition community in high-level features including, for example, lexical choice, idiomatic expressions or syntactic structures. The promise of speaker recognition in forensic applications drives development toward systems robust to channel differences by selecting features inherently robust to channel difference. Within the language recognition community, there is growing interest in differentiating not only languages but also mutually intelligible dialects of a single language. Decades of research in dialectology suggest that high-level features can enable systems to cluster speakers according to the dialects they speak. The Phanotics (Phonetic Annotation of Typicality in Conversational Speech) project seeks to identify high-level features characteristic of American dialects, annotate a corpus for these features, use the data to dialect recognition systems and also use the categorization to create better models for speaker recognition. The data, once published, should be useful to other developers of speaker and dialect recognition systems and to dialectologists and sociolinguists. We expect the methods will generalize well beyond the speakers, dialects, and languages discussed here and should, if successful, provide a model for how linguists and technology developers can collaborate in the future for the benefit of both groups and toward a deeper understanding of how languages vary and change.
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Summary

Recent years have seen increased interest within the speaker recognition community in high-level features including, for example, lexical choice, idiomatic expressions or syntactic structures. The promise of speaker recognition in forensic applications drives development toward systems robust to channel differences by selecting features inherently robust to channel difference. Within the...

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Bridging the gap between linguists and technology developers: large-scale, sociolinguistic annotation for dialect and speaker recognition

A multi-class MLLR kernel for SVM speaker recognition

March 31, 2008
  |
Conference Paper
Author:
Zahi N. Karam
…
Published in:
Proc. IEEE Int. Connf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008, pp. 4117-4120.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a Gaussian mixture model (GMM), and maximum likelihood linear regression (MLLR) adaptation is used to adapt the means of the UBM. Recent work has examined this setup for the case where a global MLLR transform is applied to all the mixture components of the GMM UBM. This work produced positive results that warrant examining this setup with multi-class MLLR adaptation, which groups the UBM mixture components into classes and applies a different transform to each class. This paper extends the MLLR/GMM framework to the multiclass case. Experiments on the NIST SRE 2006 corpus show that multi-class MLLR improves on global MLLR and that the proposed system?s performance is comparable with state of the art systems.
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Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a...

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A multi-class MLLR kernel for SVM speaker recognition

Classification methods for speaker recognition

September 1, 2007
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Book Chapter
Author:
Douglas E. Sturim
…
Published in:
Chapter in Springer Lecture Notes in Artificial Intelligence, 2007.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Automatic speaker recognition systems have a foundation built on ideas and techniques from the areas of speech science for speaker characterization, pattern recognition and engineering. In this chapter we provide an overview of the features, models, and classifiers derived from these areas that are the basis for modern automatic speaker recognition systems. We describe the components of state-of-the-art automatic speaker recognition systems, discuss application considerations and provide a brief survey of accuracy for different tasks.
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Summary

Automatic speaker recognition systems have a foundation built on ideas and techniques from the areas of speech science for speaker characterization, pattern recognition and engineering. In this chapter we provide an overview of the features, models, and classifiers derived from these areas that are the basis for modern automatic speaker...

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Classification methods for speaker recognition

Speaker verification using support vector machines and high-level features

September 1, 2007
  |
Journal Article
Author:
William M. Campbell
…
Published in:
IEEE Trans. on Audio, Speech, and Language Process., Vol. 15, No. 7, September 2007, pp. 2085-2094.
Topic:
language recognition
R&D area:
R&D group:

Summary

High-level characteristics such as word usage, pronunciation, phonotactics, prosody, etc., have seen a resurgence for automatic speaker recognition over the last several years. With the availability of many conversation sides per speaker in current corpora, high-level systems now have the amount of data needed to sufficiently characterize a speaker. Although a significant amount of work has been done in finding novel high-level features, less work has been done on modeling these features. We describe a method of speaker modeling based upon support vector machines. Current high-level feature extraction produces sequences or lattices of tokens for a given conversation side. These sequences can be converted to counts and then frequencies of -gram for a given conversation side. We use support vector machine modeling of these n-gram frequencies for speaker verification. We derive a new kernel based upon linearizing a log likelihood ratio scoring system. Generalizations of this method are shown to produce excellent results on a variety of high-level features. We demonstrate that our methods produce results significantly better than standard log-likelihood ratio modeling. We also demonstrate that our system can perform well in conjunction with standard cesptral speaker recognition systems.
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Summary

High-level characteristics such as word usage, pronunciation, phonotactics, prosody, etc., have seen a resurgence for automatic speaker recognition over the last several years. With the availability of many conversation sides per speaker in current corpora, high-level systems now have the amount of data needed to sufficiently characterize a speaker. Although...

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Speaker verification using support vector machines and high-level features

A comparison of speaker clustering and speech recognition techniques for air situational awareness

August 27, 2007
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Conference Paper
Author:
Wade Shen
…
Published in:
INTERSPEECH 2007, 27-31 August 2007, pp. 2421-2424.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

In this paper we compare speaker clustering and speech recognition techniques to the problem of understanding patterns of air traffic control communications. For a given radio transmission, our goal is to identify the talker and to whom he/she is speaking. This information, in combination with knowledge of the roles (i.e. takeoff, approach, hand-off, taxi, etc.) of different radio frequencies within an air traffic control region could allow tracking of pilots through various stages of flight, thus providing the potential to monitor the airspace in great detail. Both techniques must contend with degraded audio channels and significant non-native accents. We report results from experiments using the nn-MATC database showing 9.3% and 32.6% clustering error for speaker clustering and ASR methods respectively.
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Summary

In this paper we compare speaker clustering and speech recognition techniques to the problem of understanding patterns of air traffic control communications. For a given radio transmission, our goal is to identify the talker and to whom he/she is speaking. This information, in combination with knowledge of the roles (i.e...

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A comparison of speaker clustering and speech recognition techniques for air situational awareness

A new kernel for SVM MLLR based speaker recognition

August 27, 2007
  |
Conference Paper
Author:
Zahi N. Karam
…
Published in:
INTERSPEECH, 27-31 August 2007.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a Gaussian mixture model (GMM), and maximum likelihood linear regression (MLLR) adaptation is used to adapt the means of the UBM. We examine two possible SVM feature expansions that arise in this context: the first, a GMM supervector is constructed by stacking the means of the adapted GMM, and the second consists of the elements of the MLLR transform. We examine several kernels associated with these expansions. We show that both expansions are equivalent given an appropriate choice of kernels. Experiments performed on the NIST SRE 2006 corpus clearly highlight that our choice of kernels, which are motivated by distance metrics between GMMs, outperform ad-hoc ones. We also apply SVM nuisance attribute projection (NAP) to the kernels as a form of channel compensation and show that, with a proper choice of kernel, we achieve results comparable to existing SVM based recognizers.
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Summary

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a...

READ MORE
A new kernel for SVM MLLR based speaker recognition
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