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Finding malicious cyber discussions in social media

Summary

Today's analysts manually examine social media networks to find discussions concerning planned cyber attacks, attacker techniques and tools, and potential victims. Applying modern machine learning approaches, Lincoln Laboratory has demonstrated the ability to automatically discover such discussions from Stack Exchange, Reddit, and Twitter posts written in English.
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Summary

Today's analysts manually examine social media networks to find discussions concerning planned cyber attacks, attacker techniques and tools, and potential victims. Applying modern machine learning approaches, Lincoln Laboratory has demonstrated the ability to automatically discover such discussions from Stack Exchange, Reddit, and Twitter posts written in English.

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Iris biometric security challenges and possible solutions: for your eyes only? Using the iris as a key

Summary

Biometrics were originally developed for identification, such as for criminal investigations. More recently, biometrics have been also utilized for authentication. Most biometric authentication systems today match a user's biometric reading against a stored reference template generated during enrollment. If the reading and the template are sufficiently close, the authentication is considered successful and the user is authorized to access protected resources. This binary matching approach has major inherent vulnerabilities. An alternative approach to biometric authentication proposes to use fuzzy extractors (also known as biometric cryptosystems), which derive cryptographic keys from noisy sources, such as biometrics. In theory, this approach is much more robust and can enable cryptographic authorization. Unfortunately, for many biometrics that provide high-quality identification, fuzzy extractors provide no security guarantees. This gap arises in part because of an objective mismatch. The quality of a biometric identification is typically measured using false match rate (FMR) versus false nonmatch rate (FNMR). As a result, biometrics have been extensively optimized for this metric. However, this metric says little about the suitability of a biometric for key derivation. In this article, we illustrate a metric that can be used to optimize biometrics for authentication. Using iris biometrics as an example, we explore possible directions for improving processing and representation according to this metric. Finally, we discuss why strong biometric authentication remains a challenging problem and propose some possible future directions for addressing these challenges.
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Summary

Biometrics were originally developed for identification, such as for criminal investigations. More recently, biometrics have been also utilized for authentication. Most biometric authentication systems today match a user's biometric reading against a stored reference template generated during enrollment. If the reading and the template are sufficiently close, the authentication is...

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Characterizing phonetic transformations and acoustic differences across English dialects

Published in:
IEEE Trans. Audio, Speech, and Lang. Process., Vol. 22, No. 1, January 2014, pp. 110-24.

Summary

In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialect-specific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to convert pronunciation of one dialect to another using learned rules, recognize dialects using learned rules, retrieve dialect-specific regions, and refine linguistic rules. Potential applications of our proposed framework include computer-assisted language learning, sociolinguistics, and diagnosis tools for phonological disorders.
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Summary

In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialect-specific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to...

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Analyzing and interpreting automatically learned rules across dialects

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

Summary

In this paper, we demonstrate how informative dialect recognition systems such as acoustic pronunciation model (APM) help speech scientists locate and analyze phonetic rules efficiently. In particular, we analyze dialect-specific characteristics automatically learned from APM across two American English dialects. We show that unsupervised rule retrieval performs similarly to supervised retrieval, indicating that APM is useful in practical applications, where word transcripts are often unavailable. We also demonstrate that the top-ranking rules learned from APM generally correspond to the linguistic literature, and can even pinpoint potential research directions to refine existing knowledge. Thus, the APM system can help phoneticians analyze rules efficiently by characterizing large amounts of data to postulate rule candidates, so they can reserve time to conduct more targeted investigations. Potential applications of informative dialect recognition systems include forensic phonetics and diagnosis of spoken language disorders.
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Summary

In this paper, we demonstrate how informative dialect recognition systems such as acoustic pronunciation model (APM) help speech scientists locate and analyze phonetic rules efficiently. In particular, we analyze dialect-specific characteristics automatically learned from APM across two American English dialects. We show that unsupervised rule retrieval performs similarly to supervised...

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Assessing the speaker recognition performance of naive listeners using Mechanical Turk

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5916-5919.

Summary

In this paper we attempt to quantify the ability of naive listeners to perform speaker recognition in the context of the NIST evaluation task. We describe our protocol: a series of listening experiments using large numbers of naive listeners (432) on Amazon's Mechanical Turk that attempts to measure the ability of the average human listener to perform speaker recognition. Our goal was to compare the performance of the average human listener to both forensic experts and state-of-the- art automatic systems. We show that naive listeners vary substantially in their performance, but that an aggregation of listener responses can achieve performance similar to that of expert forensic examiners.
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Summary

In this paper we attempt to quantify the ability of naive listeners to perform speaker recognition in the context of the NIST evaluation task. We describe our protocol: a series of listening experiments using large numbers of naive listeners (432) on Amazon's Mechanical Turk that attempts to measure the ability...

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Informative dialect recognition using context-dependent pronunciation modeling

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 4396-4399.

Summary

We propose an informative dialect recognition system that learns phonetic transformation rules, and uses them to identify dialects. A hidden Markov model is used to align reference phones with dialect specific pronunciations to characterize when and how often substitutions, insertions, and deletions occur. Decision tree clustering is used to find context-dependent phonetic rules. We ran recognition tasks on 4 Arabic dialects. Not only do the proposed systems perform well on their own, but when fused with baselines they improve performance by 21-36% relative. In addition, our proposed decision-tree system beats the baseline monophone system in recovering phonetic rules by 21% relative. Pronunciation rules learned by our proposed system quantify the occurrence frequency of known rules, and suggest rule candidates for further linguistic studies.
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Summary

We propose an informative dialect recognition system that learns phonetic transformation rules, and uses them to identify dialects. A hidden Markov model is used to align reference phones with dialect specific pronunciations to characterize when and how often substitutions, insertions, and deletions occur. Decision tree clustering is used to find...

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USSS-MITLL 2010 human assisted speaker recognition

Summary

The United States Secret Service (USSS) teamed with MIT Lincoln Laboratory (MIT/LL) in the US National Institute of Standards and Technology's 2010 Speaker Recognition Evaluation of Human Assisted Speaker Recognition (HASR). We describe our qualitative and automatic speaker comparison processes and our fusion of these processes, which are adapted from USSS casework. The USSS-MIT/LL 2010 HASR results are presented. We also present post-evaluation results. The results are encouraging within the resolving power of the evaluation, which was limited to enable reasonable levels of human effort. Future ideas and efforts are discussed, including new features and capitalizing on naive listeners.
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Summary

The United States Secret Service (USSS) teamed with MIT Lincoln Laboratory (MIT/LL) in the US National Institute of Standards and Technology's 2010 Speaker Recognition Evaluation of Human Assisted Speaker Recognition (HASR). We describe our qualitative and automatic speaker comparison processes and our fusion of these processes, which are adapted from...

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Transcript-dependent speaker recognition using mixer 1 and 2

Published in:
INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, 26-30 September 2010, pp. 2102-2015.

Summary

Transcript-dependent speaker-recognition experiments are performed with the Mixer 1 and 2 read-transcription corpus using the Lincoln Laboratory speaker recognition system. Our analysis shows how widely speaker-recognition performance can vary on transcript-dependent data compared to conversational data of the same durations, given enrollment data from the same spontaneous conversational speech. A description of the techniques used to deal with the unaudited data in order to create 171 male and 198 female text-dependent experiments from the Mixer 1 and 2 read transcription corpus is given.
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Summary

Transcript-dependent speaker-recognition experiments are performed with the Mixer 1 and 2 read-transcription corpus using the Lincoln Laboratory speaker recognition system. Our analysis shows how widely speaker-recognition performance can vary on transcript-dependent data compared to conversational data of the same durations, given enrollment data from the same spontaneous conversational speech. A...

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A linguistically-informative approach to dialect recognition using dialect-discriminating context-dependent phonetic models

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 15 March 2010, pp. 5014-5017.

Summary

We propose supervised and unsupervised learning algorithms to extract dialect discriminating phonetic rules and use these rules to adapt biphones to identify dialects. Despite many challenges (e.g., sub-dialect issues and no word transcriptions), we discovered dialect discriminating biphones compatible with the linguistic literature, while outperforming a baseline monophone system by 7.5% (relative). Our proposed dialect discriminating biphone system achieves similar performance to a baseline all-biphone system despite using 25% fewer biphone models. In addition, our system complements PRLM (Phone Recognition followed by Language Modeling), verified by obtaining relative gains of 15-29% when fused with PRLM. Our work is an encouraging first step towards a linguistically-informative dialect recognition system, with potential applications in forensic phonetics, accent training, and language learning.
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Summary

We propose supervised and unsupervised learning algorithms to extract dialect discriminating phonetic rules and use these rules to adapt biphones to identify dialects. Despite many challenges (e.g., sub-dialect issues and no word transcriptions), we discovered dialect discriminating biphones compatible with the linguistic literature, while outperforming a baseline monophone system by...

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Large-scale analysis of formant frequency estimation variability in conversational telephone speech

Published in:
INTERSPEECH 2009, 6-10 September 2009.

Summary

We quantify how the telephone channel and regional dialect influence formant estimates extracted from Wavesurfer in spontaneous conversational speech from over 3,600 native American English speakers. To the best of our knowledge, this is the largest scale study on this topic. We found that F1 estimates are higher in cellular channels than those in landline, while F2 in general shows an opposite trend. We also characterized vowel shift trends in northern states in U.S.A. and compared them with the Northern city chain shift (NCCS). Our analysis is useful in forensic applications where it is important to distinguish between speaker, dialect, and channel characteristics.
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Summary

We quantify how the telephone channel and regional dialect influence formant estimates extracted from Wavesurfer in spontaneous conversational speech from over 3,600 native American English speakers. To the best of our knowledge, this is the largest scale study on this topic. We found that F1 estimates are higher in cellular...

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