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Supervector LDA - a new approach to reduced-complexity i-vector language recognition

Published in:
INTERSPEECH 2012: 13th Annual Conf. of the Int. Speech Communication Assoc., 9-13 September 2012.

Summary

In this paper, we extend our previous analysis of Gaussian Mixture Model (GMM) subspace compensation techniques using Gaussian modeling in the supervector space combined with additive channel and observation noise. We show that under the modeling assumptions of a total-variability i-vector system, full Gaussian supervector scoring can also be performed cheaply in the total subspace, and that i-vector scoring can be viewed as an approximation to this. Next, we show that covariance matrix estimation in the i-vector space can be used to generate PCA estimates of supervector covariance matrices needed for Joint Factor Analysis (JFA). Finally, we derive a new technique for reduced-dimension i-vector extraction which we call Supervector LDA (SV-LDA), and demonstrate a 100-dimensional i-vector language recognition system with equivalent performance to a 600-dimensional version at much lower complexity.
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Summary

In this paper, we extend our previous analysis of Gaussian Mixture Model (GMM) subspace compensation techniques using Gaussian modeling in the supervector space combined with additive channel and observation noise. We show that under the modeling assumptions of a total-variability i-vector system, full Gaussian supervector scoring can also be performed...

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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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Linear prediction modulation filtering for speaker recognition of reverberant speech

Published in:
Odyssey 2012, The Speaker and Language Recognition Workshop, 25-28 June 2012.

Summary

This paper proposes a framework for spectral enhancement of reverberant speech based on inversion of the modulation transfer function. All-pole modeling of modulation spectra of clean and degraded speech are utilized to derive the linear prediction inverse modulation transfer function (LP-IMTF) solution as a low-order IIR filter in the modulation envelope domain. By considering spectral estimation under speech presence uncertainty, speech presence probabilities are derived for the case of reverberation. Aside from enhancement, the LP-IMTF framework allows for blind estimation of reverberation time by extracting a minimum phase approximation of the short-time spectral channel impulse response. The proposed speech enhancement method is used as a front-end processing step for speaker recognition. When applied to the microphone condition of the NISTSRE 2010 with artificially added reverberation, the proposed spectral enhancement method yields significant improvements across a variety of performance metrics.
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Summary

This paper proposes a framework for spectral enhancement of reverberant speech based on inversion of the modulation transfer function. All-pole modeling of modulation spectra of clean and degraded speech are utilized to derive the linear prediction inverse modulation transfer function (LP-IMTF) solution as a low-order IIR filter in the modulation...

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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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Automatic detection of depression in speech using Gaussian mixture modeling with factor analysis

Summary

Of increasing importance in the civilian and military population is the recognition of Major Depressive Disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we investigate automatic classifiers of depression state, that have the important property of mitigating nuisances due to data variability, such as speaker and channel effects, unrelated to levels of depression. To assess our measures, we use a 35-speaker free-response speech database of subjects treated for depression over a six-week duration, along with standard clinical HAMD depression ratings. Preliminary experiments indicate that by mitigating nuisances, thus focusing on depression severity as a class, we can significantly improve classification accuracy over baseline Gaussian-mixture-model-based classifiers.
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Summary

Of increasing importance in the civilian and military population is the recognition of Major Depressive Disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we investigate automatic classifiers of depression state, that have the important property...

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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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Cognitive services for the user

Published in:
Chapter 10, Cognitive Radio Technology, 2009, pp. 305-324.

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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Eigen-channel compensation and discriminatively trained Gaussian mixture models for dialect and accent recognition

Published in:
Proc. INTERSPEECH 2008, 22-26 September 2008, pp. 723-726.

Summary

This paper presents a series of dialect/accent identification results for three sets of dialects with discriminatively trained Gaussian mixture models and feature compensation using eigen-channel decomposition. The classification tasks evaluated in the paper include: 1)the Chinese language classes, 2) American and Indian accented English and 3) discrimination between three Arabic dialects. The first two tasks were evaluated on the 2007 NIST LRE corpus. The Arabic discrimination task was evaluated using data derived from the LDC Arabic set collected by Appen. Analysis is performed for the English accent problem studied and an approach to open set dialect scoring is introduced. The system resulted in equal error rates at or below 10% for each of the tasks studied.
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Summary

This paper presents a series of dialect/accent identification results for three sets of dialects with discriminatively trained Gaussian mixture models and feature compensation using eigen-channel decomposition. The classification tasks evaluated in the paper include: 1)the Chinese language classes, 2) American and Indian accented English and 3) discrimination between three Arabic...

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