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Exploring the impact of advanced front-end processing on NIST speaker recognition microphone tasks

Summary

The NIST speaker recognition evaluation (SRE) featured microphone data in the 2005-2010 evaluations. The preprocessing and use of this data has typically been performed with telephone bandwidth and quantization. Although this approach is viable, it ignores the richer properties of the microphone data-multiple channels, high-rate sampling, linear encoding, ambient noise properties, etc. In this paper, we explore alternate choices of preprocessing and examine their effects on speaker recognition performance. Specifically, we consider the effects of quantization, sampling rate, enhancement, and two-channel speech activity detection. Experiments on the NIST 2010 SRE interview microphone corpus demonstrate that performance can be dramatically improved with a different preprocessing chain.
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Summary

The NIST speaker recognition evaluation (SRE) featured microphone data in the 2005-2010 evaluations. The preprocessing and use of this data has typically been performed with telephone bandwidth and quantization. Although this approach is viable, it ignores the richer properties of the microphone data-multiple channels, high-rate sampling, linear encoding, ambient noise...

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The MITLL NIST LRE 2011 language recognition system

Summary

This paper presents a description of the MIT Lincoln Laboratory (MITLL) language recognition system developed for the NIST 2011 Language Recognition Evaluation (LRE). The submitted system consisted of a fusion of four core classifiers, three based on spectral similarity and one based on tokenization. Additional system improvements were achieved following the submission deadline. In a major departure from previous evaluations, the 2011 LRE task focused on closed-set pairwise performance so as to emphasize a system's ability to distinguish confusable language pairs. Results are presented for the 24-language confusable pair task at test utterance durations of 30, 10, and 3 seconds. Results are also shown using the standard detection metrics (DET, minDCF) and it is demonstrated the previous metrics adequately cover difficult pair performance. On the 30 s 24-language confusable pair task, the submitted and post-evaluation systems achieved average costs of 0.079 and 0.070 and standard detection costs of 0.038 and 0.033.
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Summary

This paper presents a description of the MIT Lincoln Laboratory (MITLL) language recognition system developed for the NIST 2011 Language Recognition Evaluation (LRE). The submitted system consisted of a fusion of four core classifiers, three based on spectral similarity and one based on tokenization. Additional system improvements were achieved following...

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A new perspective on GMM subspace compensation based on PPCA and Wiener filtering

Published in:
2011 INTERSPEECH, 27-31 August 2011, pp. 145-148.

Summary

We present a new perspective on the subspace compensation techniques that currently dominate the field of speaker recognition using Gaussian Mixture Models (GMMs). Rather than the traditional factor analysis approach, we use Gaussian modeling in the sufficient statistic supervector space combined with Probabilistic Principal Component Analysis (PPCA) within-class and shared across class covariance matrices to derive a family of training and testing algorithms. Key to this analysis is the use of two noise terms for each speech cut: a random channel offset and a length dependent observation noise. Using the Wiener filtering perspective, formulas for optimal train and test algorithms for Joint Factor Analysis (JFA) are simple to derive. In addition, we can show that an alternative form of Wiener filtering results in the i-vector approach, thus tying together these two disparate techniques.
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Summary

We present a new perspective on the subspace compensation techniques that currently dominate the field of speaker recognition using Gaussian Mixture Models (GMMs). Rather than the traditional factor analysis approach, we use Gaussian modeling in the sufficient statistic supervector space combined with Probabilistic Principal Component Analysis (PPCA) within-class and shared...

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Language recognition via i-vectors and dimensionality reduction

Published in:
2011 INTERSPEECH, 27-31 August 2011, pp. 857-860.

Summary

In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification. Various techniques are employed to extract the most salient features in the lower dimensional i-vector space and the system developed results in excellent performance on the 2009 LRE evaluation set without the need for any post-processing or backend techniques. Additional performance gains are observed when the system is combined with other acoustic systems.
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Summary

In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification. Various techniques are employed to extract the most salient features in the lower dimensional i-vector space and the system developed results in excellent performance on the...

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

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5272-5275.

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 MITLL NIST LRE 2009 language recognition system

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 15 March 2010, pp. 4994-4997.

Summary

This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2009 Language Recognition Evaluation (LRE). This system consists of a fusion of three core recognizers, two based on spectral similarity and one based on tokenization. The 2009 LRE differed from previous ones in that test data included narrowband segments from worldwide Voice of America broadcasts as well as conventional recorded conversational telephone speech. Results are presented for the 23-language closed-set and open-set detection tasks at the 30, 10, and 3 second durations along with a discussion of the language-pair task. On the 30 second 23-language closed set detection task, the system achieved a 1.64 average error rate.
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Summary

This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2009 Language Recognition Evaluation (LRE). This system consists of a fusion of three core recognizers, two based on spectral similarity and one based on tokenization. The 2009 LRE differed from previous ones in...

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The MIT Lincoln Laboratory 2008 speaker recognition system

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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Gaussian mixture models

Published in:
Article in Encyclopedia of Biometrics, 2009, pp. 659-63. DOI: https://doi.org/10.1007/978-0-387-73003-5_196

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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Language, dialect, and speaker recognition using Gaussian mixture models on the cell processor

Published in:
Twelfth Annual High Performance Embedded Computing Workshop, HPEC 2008, 23-25 September 2008.

Summary

Automatic recognition systems are commonly used in speech processing to classify observed utterances by the speaker's identity, dialect, and language. These problems often require high processing throughput, especially in applications involving multiple concurrent incoming speech streams, such as in datacenter-level processing. Recent advances in processor technology allow multiple processors to reside within the same chip, allowing high performance per watt. Currently the Cell Broadband Engine has the leading performance-per-watt specifications in its class. Each Cell processor consists of a PowerPC Processing Element (PPE) working together with eight Synergistic Processing Elements (SPE). The SPEs have 256KB of memory (local store), which is used for storing both program and data. This paper addresses the implementation of language, dialect, and speaker recognition on the Cell architecture. Classically, the problem of performing speech-domain recognition has been approached as embarrassingly parallel, with each utterance being processed in parallel to the others. As we will discuss, efficient processing on the Cell requires a different approach, whereby computation and data for each utterance are subdivided to be handled by separate processors. We present a computational model for automatic recognition on the Cell processor that takes advantage of its architecture, while mitigating its limitations. Using the proposed design, we predict a system able to concurrently score over 220 real-time speech streams on a single Cell.
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Summary

Automatic recognition systems are commonly used in speech processing to classify observed utterances by the speaker's identity, dialect, and language. These problems often require high processing throughput, especially in applications involving multiple concurrent incoming speech streams, such as in datacenter-level processing. Recent advances in processor technology allow multiple processors to...

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A comparison of subspace feature-domain methods for language recognition

Summary

Compensation of cepstral features for mismatch due to dissimilar train and test conditions has been critical for good performance in many speech applications. Mismatch is typically due to variability from changes in speaker, channel, gender, and environment. Common methods for compensation include RASTA, mean and variance normalization, VTLN, and feature warping. Recently, a new class of subspace methods for model compensation have become popular in language and speaker recognition--nuisance attribute projection (NAP) and factor analysis. A feature space version of latent factor analysis has been proposed. In this work, a feature space version of NAP is presented. This new approach, fNAP, is contrasted with feature domain latent factor analysis (fLFA). Both of these methods are applied to a NIST language recognition task. Results show the viability of the new fNAP method. Also, results indicate when the different methods perform best.
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Summary

Compensation of cepstral features for mismatch due to dissimilar train and test conditions has been critical for good performance in many speech applications. Mismatch is typically due to variability from changes in speaker, channel, gender, and environment. Common methods for compensation include RASTA, mean and variance normalization, VTLN, and feature...

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