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Speaker Recognition Using Real vs Synthetic Parallel Data for DNN Channel Compensation(891.97 KB)

Date:
September 8, 2016
Published in:
Proceedings of Interspeech 2016, San Francisco, Calif.
Type:
Conference Paper

Summary

Recently there has been a great deal of interest in using deep neural networks (DNNs) for channel compensation under reverberant or noisy channel conditions such as those found in microphone data. This paper compares the use of real and synthetic data for training denoising DNNs for multi-microphone speaker recognition.

The AFRL-MITLL WMT16 News-Translation Task Systems(375.46 KB)

Date:
August 16, 2016
Published in:
Proceedings of the 11th Workshop on Machine Translation (WMT’16)
Type:
Conference Paper

Summary

This paper describes the AFRL-MITLL statistical machine translation systems and the improvements that were developed during the WMT16 evaluation campaign. New techniques applied this year include Neural Machine Translation, a unique selection process for language modelling data, additional out-of-vocabulary transliteration techniques, and morphology generation.

Corpora for the Evaluation of Robust Speaker Recognition Systems(177.37 KB)

Date:
August 10, 2016
Published in:
Proceedings of Interspeech 2016, San Francisco, Calif.
Type:
Conference Paper

Summary

The goal of this paper is to describe significant corpora available to support speaker recognition research and evaluation, along with details about the corpora collection and design. We describe the attributes of high-quality speaker recognition corpora. Considerations of the application, domain, and performance metrics are also discussed.

Cross-Domain Entity Resolution in Social Media(416.42 KB)

Date:
July 11, 2016
Published in:
Proceedings of Fourth International Workshop on Natural Language Processing for Social Media (SocialNLP 2016)
Type:
Conference Paper

Summary

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques.

Charting a Security Landscape in the Clouds: Data Protection and Collaboration in Cloud Storage(1.6 MB)

Date:
July 7, 2016
Published in:
MIT Lincoln Laboratory Technical Report 1210
Type:
Technical Report
Topic:

Summary

This report surveys different approaches to securely storing and sharing data in the cloud based on traditional notions of security: confidentiality, integrity, and availability, with the main focus on confidentiality. An appendix discusses the related notion of how users can securely authenticate to cloud providers.

Collaborative Data Analysis and Discovery for Cyber Security

Date:
June 22, 2016
Published in:
Proceedings of the 12th Symposium on Usable Privacy and Security (SOUPS 2016)
Type:
Conference Paper
Topic:

Summary

In this paper, we present the Cyber Analyst Real-Time Integrated Notebook Application (CARINA). CARINA is a collaborative investigation system that aids in decision making by co-locating the analysis environment with centralized cyber data sources, and providing next generation analysts with increased visibility to the work of others.

The MITLL NIST LRE 2015 Language Recognition System

Date:
June 21, 2016
Published in:
Proceedings of Odyssey 2016, Bilbao, Spain
Type:
Conference Paper

Summary

In this paper we describe the most recent MIT Lincoln Laboratory language recognition system developed for the NIST 2015 Language Recognition Evaluation (LRE). The submission features a fusion of five core classifiers, with most systems developed in the context of an i-vector framework.

Channel Compensation for Speaker Recognition using MAP Adapted PLDA and Denoising DNNs(688.37 KB)

Date:
June 21, 2016
Published in:
Proceedings of Odyssey 2016, Bilbao, Spain
Type:
Conference Paper

Summary

Over several decades, speaker recognition performance has steadily improved for applications using telephone speech. For new data applications, we would like to effectively use existing telephone data to build systems with high accuracy while maintaining good performance on existing telephone tasks. In this paper we compare and combine approaches to compensate models parameters and features for this purpose.

A Vocal Modulation Model with Application to Predicting Depression Severity(2.33 MB)

Date:
June 14, 2016
Published in:
Proceedings of Body Sensor Networks 2016, San Francisco, Calif.
Type:
Conference Paper
Topic:

Summary

Speech provides a potential simple and noninvasive “on-body” means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area.

BubbleNet: A Cyber Security Dashboard for Visualizing Patterns

Date:
June 6, 2016
Published in:
Proceeding of 2016 Eurographics Conference on Visualization (EuroVis)
Type:
Conference Paper
Topic:

Summary

The field of cyber security is faced with ever-expanding amounts of data and a constant barrage of cyber attacks. Within this space, we have designed BubbleNet as a cyber security dashboard to help network analysts identify and summarize patterns within the data.