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Advocate: a distributed architecture for speech-to-speech translation

June 1, 2009
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Journal Article
Author:
Arya R. Aminzadeh
…
Published in:
Lincoln Laboratory Journal, Vol. 18, No. 1, June 2009, pp. 54-65.
Topic:
machine translation
R&D area:
R&D group:

Summary

Advocate is a set of communications application programming interfaces and service wrappers that serve as a framework for creating complex and scalable real-time software applications from component processing algorithms. Advocate can be used for a variety of distributed processing applications, but was initially designed to use existing speech processing and machine translation components in the rapid construction of large-scale speech-to-speech translation systems. Many such speech-to-speech translation applications require real-time processing, and Advocate provides this speed with low-latency communication between services.
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Summary

Advocate is a set of communications application programming interfaces and service wrappers that serve as a framework for creating complex and scalable real-time software applications from component processing algorithms. Advocate can be used for a variety of distributed processing applications, but was initially designed to use existing speech processing and...

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Advocate: a distributed architecture for speech-to-speech translation

Advocate: a distributed voice-oriented computing architecture

May 31, 2009
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Conference Paper
Author:
Arya R. Aminzadeh
…
Published in:
North American Chapter of the Association for Computational Linguistics - Human Language Technologies Conf. (NAACL HLT 2009), 31 May - 5 June 2009.
Topic:
machine translation
R&D area:
R&D group:

Summary

Advocate is a lightweight and easy-to-use computing architecture that supports real-time, voice-oriented computing. It is designed to allow the combination of multiple speech and language processing components to create cohesive distributed applications. It is scalable, supporting local processing of all NLP/speech components when sufficient processing resources are available to one machine, or fully distributed/networked processing over an arbitrarily large compute structure when more compute resources are needed. Advocate is designed to operate in a large distributed test-bed in which an arbitrary number of NLP/speech services interface with an arbitrary number of Advocate clients applications. In this configuration, each Advocate client application employs automatic service discovery, calling them as required.
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Summary

Advocate is a lightweight and easy-to-use computing architecture that supports real-time, voice-oriented computing. It is designed to allow the combination of multiple speech and language processing components to create cohesive distributed applications. It is scalable, supporting local processing of all NLP/speech components when sufficient processing resources are available to one...

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Advocate: a distributed voice-oriented computing architecture

Modeling and detection techniques for counter-terror social network analysis and intent recognition

March 7, 2009
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Conference Paper
Author:
Clifford J. Weinstein
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Published in:
2009 IEEE Aerospace Conf., March 7-14 2009.
Topic:
social network
R&D area:
R&D group:

Summary

In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less progress has been made in the development of automated tools for analyzing the results of information extraction to ?connect the dots.? Hence, our Counter-Terror Social Network Analysis and Intent Recognition (CT-SNAIR) work focuses on development of automated techniques and tools for detection and tracking of dynamically-changing terrorist networks as well as recognition of capability and potential intent. In addition to obtaining and working with real data for algorithm development and test, we have a major focus on modeling and simulation of terrorist attacks based on real information about past attacks. We describe the development and application of a new Terror Attack Description Language (TADL), which is used as a basis for modeling and simulation of terrorist attacks. Examples are shown which illustrate the use of TADL and a companion simulator based on a Hidden Markov Model (HMM) structure to generate transactions for attack scenarios drawn from real events. We also describe our techniques for generating realistic background clutter traffic to enable experiments to estimate performance in the presence of a mix of data. An important part of our effort is to produce scenarios and corpora for use in our own research, which can be shared with a community of researchers in this area. We describe our scenario and corpus development, including specific examples from the September 2004 bombing of the Australian embassy in Jakarta and a fictitious scenario which was developed in a prior project for research in social network analysis. The scenarios can be created by subject matter experts using a graphical editing tool. Given a set of time ordered transactions between actors, we employ social network analysis (SNA) algorithms as a filtering step to divide the actors into distinct communities before determining intent. This helps reduce clutter and enhances the ability to determine activities within a specific group. For modeling and simulation purposes, we generate random networks with structures and properties similar to real-world social networks. Modeling of background traffic is an important step in generating classifiers that can separate harmless activities from suspicious activity. An algorithm for recognition of simulated potential attack scenarios in clutter based on Support Vector Machine (SVM) techniques is presented. We show performance examples, including probability of detection versus probability of false alarm tradeoffs, for a range of system parameters.
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Summary

In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less...

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Modeling and detection techniques for counter-terror social network analysis and intent recognition

Forensic speaker recognition: a need for caution

March 1, 2009
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Journal Article
Author:
Joseph P. Campbell Jr
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Published in:
IEEE Signal Process. Mag., Vol. 26, No. 2, March 2009, pp. 95-103.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

There has long been a desire to be able to identify a person on the basis of his or her voice. For many years, judges, lawyers, detectives, and law enforcement agencies have wanted to use forensic voice authentication to investigate a suspect or to confirm a judgment of guilt or innocence. Challenges, realities, and cautions regarding the use of speaker recognition applied to forensic-quality samples are presented.
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Summary

There has long been a desire to be able to identify a person on the basis of his or her voice. For many years, judges, lawyers, detectives, and law enforcement agencies have wanted to use forensic voice authentication to investigate a suspect or to confirm a judgment of guilt or...

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Forensic speaker recognition: a need for caution

Extending the dynamic range of RF receivers using nonlinear equalization

February 8, 2009
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Conference Paper
Author:
Joel I. Goodman
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Published in:
IEEE Conf. on Waveform Diversity & Design, 8 February 2009, pp. 224-228.
Topic:
signal processing
R&D area:
R&D group:

Summary

Systems currently being developed to operate across wide bandwidths with high sensitivity requirements are limited by the inherent dynamic range of a receiver's analog and mixed-signal components. To increase a receiver's overall linearity, we have developed a digital NonLinear EQualization (NLEQ) processor which is capable of extending a receiver's dynamic range from one to three orders of magnitude. In this paper we describe the NLEQ architecture and present measurements of its performance.
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Summary

Systems currently being developed to operate across wide bandwidths with high sensitivity requirements are limited by the inherent dynamic range of a receiver's analog and mixed-signal components. To increase a receiver's overall linearity, we have developed a digital NonLinear EQualization (NLEQ) processor which is capable of extending a receiver's dynamic...

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Extending the dynamic range of RF receivers using nonlinear equalization

Cognitive services for the user

January 1, 2009
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Book Chapter
Author:
Joseph P. Campbell Jr
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Published in:
Chapter 10, Cognitive Radio Technology, 2009, pp. 305-324.
Topic:
biometrics
R&D area:
R&D group:

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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Cognitive services for the user

Gaussian mixture models

January 1, 2009
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Journal Article
Author:
Douglas A. Reynolds
Published in:
Article in Encyclopedia of Biometrics, 2009, pp. 659-63. DOI: https://doi.org/10.1007/978-0-387-73003-5_196
Topic:
speaker recognition
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R&D group:

Summary

A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model.
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Summary

A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker...

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Gaussian mixture models

Low-resource speech translation of Urdu to English using semi-supervised part-of-speech tagging and transliteration

December 15, 2008
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Conference Paper
Author:
Arya R. Aminzadeh
…
Published in:
SLT 2008, IEEE Spoken Language Technology Workshop 2008, 15-10 December 2008, pp. 265-268.
Topic:
machine translation
R&D area:
R&D group:

Summary

This paper describes the construction of ASR and MT systems for translation of speech from Urdu into English. As both Urdu pronunciation lexicons and Urdu-English bitexts are sparse, we employ several techniques that make use of semi-supervised annotation to improve ASR and MT training. Specifically, we describe 1) the construction of a semi-supervised HMM-based part-of-speech tagger that is used to train factored translation models and 2) the use of an HMM-based transliterator from which we derive a spelling-to-pronunciation model for Urdu used in ASR training. We describe experiments performed for both ASR and MT training in the context of the Urdu-to-English task of the NIST MT08 Evaluation and we compare methods making use of additional annotation with standard statistical MT and ASR baselines.
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Summary

This paper describes the construction of ASR and MT systems for translation of speech from Urdu into English. As both Urdu pronunciation lexicons and Urdu-English bitexts are sparse, we employ several techniques that make use of semi-supervised annotation to improve ASR and MT training. Specifically, we describe 1) the construction...

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Low-resource speech translation of Urdu to English using semi-supervised part-of-speech tagging and transliteration

Efficient speech translation through confusion network decoding

November 1, 2008
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Journal Article
Author:
Nicola Bertoldi
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Published in:
IEEE Trans. Audio Speech Lang. Proc., Vol. 16, No. 8, November 2008, pp. 1696-1705.
Topic:
speech recognition
R&D area:
R&D group:

Summary

This paper describes advances in the use of confusion networks as interface between automatic speech recognition and machine translation. In particular, it presents a decoding algorithm for confusion networks which results as an extension of a state-of-the-art phrase-based text translation decoder. The confusion network decoder significantly improves both in efficiency and performance over previous work along this direction, and outperforms the background text translation system. Experimental results in terms of translation accuracy and decoding efficiency are reported for the task of translating plenary speeches of the European Parliament from Spanish to English and from English to Spanish.
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Summary

This paper describes advances in the use of confusion networks as interface between automatic speech recognition and machine translation. In particular, it presents a decoding algorithm for confusion networks which results as an extension of a state-of-the-art phrase-based text translation decoder. The confusion network decoder significantly improves both in efficiency...

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Efficient speech translation through confusion network decoding

A polyphase nonlinear equalization architecture and semi-blind identification method

October 26, 2008
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Conference Paper
Author:
Benjamin A. Miller
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Published in:
42th Asilomar Conf. on Signals, Systems, and Computers, 27 October 2008, pp. 593-597.
Topic:
signal processing
R&D area:
R&D group:

Summary

In this paper, we present an architecture and semiblind identification method for a polyphase nonlinear equalizer (pNLEQ). Such an equalizer is useful for extending the dynamic range of time-interleaved analog-to-digital converters (ADCs). Our proposed architecture is a polyphase extension to other architectures that partition the Volterra kernel into small nonlinear filters with relatively low computational complexity. Our semi-blind identification technique addresses important practical concerns in the equalizer identification process. We describe our architecture and demonstrate its performance with measured results when applied to a National Semiconductor ADC081000.
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Summary

In this paper, we present an architecture and semiblind identification method for a polyphase nonlinear equalizer (pNLEQ). Such an equalizer is useful for extending the dynamic range of time-interleaved analog-to-digital converters (ADCs). Our proposed architecture is a polyphase extension to other architectures that partition the Volterra kernel into small nonlinear...

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A polyphase nonlinear equalization architecture and semi-blind identification method
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