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Approaches for Language Identification in Mismatched Environments

Summary

In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system.
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Summary

In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system.
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Bootstrapping and Maintaining Trust in the Cloud(469.63 KB)

Published in:
Proceedings of the 32nd Annual Computer Security Applications Conference, ACSAC 2016

Summary

Today's infrastructure as a service (IaaS) cloud environments rely upon full trust in the provider to secure applications and data. In this paper we introduce keylime, a scalable trusted cloud key management system. Keylime provides an end-to-end solution for both bootstrapping hardware rooted cryptographic identities for IaaS nodes and for system integrity monitoring of those nodes via periodic attestation.
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Summary

Today's infrastructure as a service (IaaS) cloud environments rely upon full trust in the provider to secure applications and data. In this paper we introduce keylime, a scalable trusted cloud key management system. Keylime provides an end-to-end solution for both bootstrapping hardware rooted cryptographic identities for IaaS nodes and for...
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Leveraging Data Provenance to Enhance Cyber Resilience(273.48 KB)

Summary

Creating bigger and better walls to keep adversaries out of our systems has been a failing strategy. The recent attacks against Target and Sony Pictures, to name a few, further emphasize this. Data provenance is a critical technology in building resilient systems that will allow systems to recover from attackers that manage to overcome the “hard-shell” defenses. In this paper, we provide background information on data provenance, details on provenance collection, analysis, and storage techniques and challenges.
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Summary

Creating bigger and better walls to keep adversaries out of our systems has been a failing strategy. The recent attacks against Target and Sony Pictures, to name a few, further emphasize this. Data provenance is a critical technology in building resilient systems that will allow systems to recover from attackers...
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Detecting Depression using Vocal, Facial and Semantic Communication Cues(308.97 KB)

Summary

Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. These changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities.
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Summary

Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. These changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are...
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Multi-Modal Audio, Video, and Physiological Sensor Learning for Continuous Emotion Prediction(451.61 KB)

Summary

The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. This paper provides an overview of our AVEC Emotion Challenge system, which uses multi-feature learning and fusion across all available modalities.
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Summary

The automatic determination of emotional state from multimedia content is an inherently challenging problem with a broad range of applications including biomedical diagnostics, multimedia retrieval, and human computer interfaces. This paper provides an overview of our AVEC Emotion Challenge system, which uses multi-feature learning and fusion across all available modalities.
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How Deep Neural Networks Can Improve Emotion Recognition on Video Data(547.86 KB)

Published in:
Proceedings of 2016 IEEE International Conference on Image Processing (ICIP)

Summary

There have been many impressive results obtained using deep learning for emotion recognition tasks in the last few years. In this work, we present a system that performs emotion recognition on video data using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
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Summary

There have been many impressive results obtained using deep learning for emotion recognition tasks in the last few years. In this work, we present a system that performs emotion recognition on video data using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
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I-Vector Speaker and Language Recognition System on Android,

Published in:
Proceedings of IEEE High Performance Extreme Computing Conference (HPEC '16)

Summary

I-Vector based speaker and language identification provides state of the art performance. However, this comes as a more computationally complex solution, which can often lead to challenges in resource-limited devices, such as phones or tablets. We present the implementation of an I-Vector speaker and language recognition system on the Android platform in the form of a fully functional application that allows speaker enrollment and language/speaker scoring within mobile contexts.
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Summary

I-Vector based speaker and language identification provides state of the art performance. However, this comes as a more computationally complex solution, which can often lead to challenges in resource-limited devices, such as phones or tablets. We present the implementation of an I-Vector speaker and language recognition system on the Android...
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High-throughput ingest of data provenance records in Accumulo

Published in:
HPEC 2016: IEEE Conf. on High Performance Extreme Computing, 13-15 September 2016.

Summary

Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must be stored somewhere where it can be queried. This problem becomes even more challenging when considering a network of provenance-aware machines all collecting this metadata. In this paper, we investigate the use of D4M and Accumulo to support high-throughput data ingest of whole-system provenance data. We find that we are able to ingest 3,970 graph components per second. Centrally storing the provenance metadata allows us to build systems that can detect and respond to data integrity attacks that are captured by the provenance system.
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Summary

Whole-system data provenance provides deep insight into the processing of data on a system, including detecting data integrity attacks. The downside to systems that collect whole-system data provenance is the sheer volume of data that is generated under many heavy workloads. In order to make provenance metadata useful, it must...
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Relation of Automatically Extracted Formant Trajectories with Intelligibility Loss and Speaking Rate Decline in Amyotrophic Lateral Sclerosis(906.23 KB)

Summary

Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS.
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Summary

Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS.
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Relating estimated cyclic spectral peak frequency to measured epilarynx length using Magnetic Resonance Imaging(272.05 KB)

Published in:
Proceedings of Interspeech 2016, San Francisco, Calif.

Summary

The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. Recent spectral processing techniques isolate a unique resonance with characteristics of the epilarynx previously shown via simulation, specifically cyclicity. Using Magnetic Resonance Imaging (MRI), the present work relates this estimated cyclic peak frequency to measured epilarynx length.
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Summary

The epilarynx plays an important role in speech production, carrying information about the individual speaker and manner of articulation. Recent spectral processing techniques isolate a unique resonance with characteristics of the epilarynx previously shown via simulation, specifically cyclicity. Using Magnetic Resonance Imaging (MRI), the present work relates this estimated cyclic...
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