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Analysis of factors affecting system performance in the ASpIRE challenge

Published in:
2015 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2015, 13-17 December 2015.

Summary

This paper presents an analysis of factors affecting system performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In particular, overall word error rate (WER) of the solver systems is analyzed as a function of room, distance between talker and microphone, and microphone type. We also analyze speech activity detection performance of the solver systems and investigate its relationship to WER. The primary goal of the paper is to provide insight into the factors affecting system performance in the ASpIRE evaluation set across many systems given annotations and metadata that are not available to the solvers. This analysis will inform the design of future challenges and provide insight into the efficacy of current solutions addressing noisy reverberant speech in mismatched conditions.
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Summary

This paper presents an analysis of factors affecting system performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In particular, overall word error rate (WER) of the solver systems is analyzed as a function of room, distance between talker and microphone, and microphone type. We also analyze speech...

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NetProf iOS pronunciation feedback demonstration

Published in:
IEEE Automatic Speech Recognition and Understanding Workshop, ASRU, 13 December 2015.

Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are not widely studied in the U.S. Many students struggle to attain speaking fluency and proper pronunciation. Teaching pronunciation is a time-intensive task for teachers that requires them to give individual feedback to students during classroom hours. This limits the time teachers can spend imparting other information, and students may feel embarrassed or inhibited when they practice with their classmates. Given the demand for students educated in foreign languages and the limited number of qualified teachers in languages of interest, there is a growing need for computer-based tools students can use to practice and receive feedback at their own pace and schedule. Most existing tools are limited to listening to pre-recorded audio with limited or nonexistent support for pronunciation feedback. MIT Lincoln Laboratory has developed a new tool, Net Pronunciation Feedback (NetProF), to address these challenges and improve student pronunciation and general language fluency.
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Summary

One of the greatest challenges for an adult learning a new language is gaining the ability to distinguish and produce foreign sounds. The US Government trains 3,600 enlisted soldiers a year at the Defense Language Institute Foreign Language Center (DLIFLC) in languages critical to national security, most of which are...

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Assessing functional neural connectivity as an indicator of cognitive performance

Published in:
5th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2015, 11-12 December 2015.

Summary

Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity on a working memory task. We provide two novel approaches for characterizing functional network connectivity from electroencephalography (EEG), and compare these features to the average power across frequency bands in EEG channels. Our first novel approach represents functional connectivity structure through the distribution of eigenvalues making up channel coherence matrices in multiple frequency bands. Our second approach creates a connectivity network at each frequency band, and assesses variability in average path lengths of connected components and degree across the network. Failures in digit and sentence recall on single trials are detected using a Gaussian classifier for each feature set, at each frequency band. The classifier results are then fused across frequency bands, with the resulting detection performance summarized using the area under the receiver operating characteristic curve (AUC) statistic. Fused AUC results of 0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall failure are obtained from the connectivity structure, graph variability, and channel power features respectively.
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Summary

Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity...

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Multimodal sparse coding for event detection

Published in:
Neural Information Processing Multimodal Machine Learning Workshop, NIPS 2015, 7-12 December 2015.

Summary

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such as GMM supervectors and sparse RBM. We report the cross-validated classification accuracy and mean average precision of the MED system trained on features learned from our unimodal and multimodal settings for a subset of the TRECVID MED 2014 dataset.
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Summary

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature...

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Fast online learning of antijamming and jamming strategies

Published in:
2015 IEEE Global Communications Conf., 6-10 December 2015.

Summary

Competing Cognitive Radio Network (CCRN) coalesces communicator (comm) nodes and jammers to achieve maximal networking efficiency in the presence of adversarial threats. We have previously developed two contrasting approaches for CCRN based on multi-armed bandit (MAB) and Qlearning. Despite their differences, both approaches have shown to achieve optimal throughput performance. This paper addresses a harder class of problems where channel rewards are time-varying such that learning based on stochastic assumptions cannot guarantee the optimal performance. This new problem is important because an intelligent adversary will likely introduce dynamic changepoints, which can make our previous approaches ineffective. We propose a new, faster learning algorithm using online convex programming that is computationally simpler and stateless. According to our empirical results, the new algorithm can almost instantly find an optimal strategy that achieves the best steady-state channel rewards.
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Summary

Competing Cognitive Radio Network (CCRN) coalesces communicator (comm) nodes and jammers to achieve maximal networking efficiency in the presence of adversarial threats. We have previously developed two contrasting approaches for CCRN based on multi-armed bandit (MAB) and Qlearning. Despite their differences, both approaches have shown to achieve optimal throughput performance...

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The MITLL-AFRL IWSLT 2015 Systems

Summary

This report summarizes the MITLL-AFRL MT, ASR and SLT systems and the experiments run using them during the 2015 IWSLT evaluation campaign. We build on the progress made last year, and additionally experimented with neural MT, unknown word processing, and system combination. We applied these techniques to translating Chinese to English and English to Chinese. ASR systems are also improved by reining improvements developed last year. Finally, we combine our ASR and MT systems to produce a English to Chinese SLT system.
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Summary

This report summarizes the MITLL-AFRL MT, ASR and SLT systems and the experiments run using them during the 2015 IWSLT evaluation campaign. We build on the progress made last year, and additionally experimented with neural MT, unknown word processing, and system combination. We applied these techniques to translating Chinese to...

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Spyglass: demand-provisioned Linux containers for private network access

Published in:
Proc. 29th Large Installation System Administration Conf., LISA, 8-13 November 2015.

Summary

System administrators are required to access the privileged, or "super-user," interfaces of computing, networking, and storage resources they support. This low-level infrastructure underpins most of the security tools and features common today and is assumed to be secure. A malicious system administrator or malware on the system administrator's client system can silently subvert this computing infrastructure. In the case of cloud system administrators, unauthorized privileged access has the potential to cause grave damage to the cloud provider and their customers. In this paper, we describe Spyglass, a tool for managing, securing, and auditing administrator access to private or sensitive infrastructure networks by creating on-demand bastion hosts inside of Linux containers. These on-demand bastion containers differ from regular bastion hosts in that they are nonpersistent and last only for the duration of the administrator's access. Spyglass also captures command input and screen output of all administrator activities from outside the container, allowing monitoring of sensitive infrastructure and understanding of the actions of an adversary in the event of a compromise. Through our evaluation of Spyglass for remote network access, we show that it is more difficult to penetrate than existing solutions, does not introduce delays or major workflow changes, and increases the amount of tamper-resistant auditing information that is captured about a system administrator's access.
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Summary

System administrators are required to access the privileged, or "super-user," interfaces of computing, networking, and storage resources they support. This low-level infrastructure underpins most of the security tools and features common today and is assumed to be secure. A malicious system administrator or malware on the system administrator's client system...

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Improved hidden clique detection by optimal linear fusion of multiple adjacency matrices

Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.

Summary

Graph fusion has emerged as a promising research area for addressing challenges associated with noisy, uncertain, multi-source data. While many ad-hoc graph fusion techniques exist in the current literature, an analytical approach for analyzing the fundamentals of the graph fusion problem is lacking. We consider the setting where we are given multiple Erdos-Renyi modeled adjacency matrices containing a common hidden or planted clique. The objective is to combine them linearly so that the principal eigenvectors of the resulting matrix best reveal the vertices associated with the clique. We utilize recent results from random matrix theory to derive the optimal weighting coefficients and use these insights to develop a data-driven fusion algorithm. We demonstrate the improved performance of the algorithm relative to other simple heuristics.
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Summary

Graph fusion has emerged as a promising research area for addressing challenges associated with noisy, uncertain, multi-source data. While many ad-hoc graph fusion techniques exist in the current literature, an analytical approach for analyzing the fundamentals of the graph fusion problem is lacking. We consider the setting where we are...

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Residuals-based subgraph detection with cue vertices

Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.

Summary

A common problem in modern graph analysis is the detection of communities, an example of which is the detection of a single anomalously dense subgraph. Recent results have demonstrated a fundamental limit for this problem when using spectral analysis of modularity. In this paper, we demonstrate the implication of these results on subgraph detection when a cue vertex is provided, indicating one of the vertices in the community of interest. Several recent algorithms for local community detection are applied in this context, and we compare their empirical performance to that of the simple method used to derive the theoretical detection limits.
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Summary

A common problem in modern graph analysis is the detection of communities, an example of which is the detection of a single anomalously dense subgraph. Recent results have demonstrated a fundamental limit for this problem when using spectral analysis of modularity. In this paper, we demonstrate the implication of these...

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Sampling operations on big data

Published in:
2015 Asilomar Conf. on Signals, Systems and Computers, 8-11 November 2015.

Summary

The 3Vs -- Volume, Velocity and Variety -- of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades to compress signals are often undefined in data that is characterized by heterogeneity, high dimensionality, and lack of known structure. In this article, we describe and demonstrate an approach to sample large datasets such as social media data. We evaluate the effect of sampling on a common predictive analytic: link prediction. Our results indicate that greatly sampling a dataset can still yield meaningful link prediction results.
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Summary

The 3Vs -- Volume, Velocity and Variety -- of Big Data continues to be a large challenge for systems and algorithms designed to store, process and disseminate information for discovery and exploration under real-time constraints. Common signal processing operations such as sampling and filtering, which have been used for decades...

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