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The present invention describes a system and method for communicating voice and data over a packet-switched network that is adapted to coexist and communicate with a legacy PSTN. The system permits packet switching of voice calls and data calls through a data network from and to any of a LEC, a customer facility or a direct IP connection on the data network. The system includes soft switch sites, gateway sites, a data network, a provisioning component, a network event component and a network management component. The system interfaces with customer facilities (e.g., a PBX), carrier facilities (e.g., a LEC) and legacy signaling networks (e.g., SS7) to handle calls between any combination of on-network and off-network callers. The soft switch sites provide the core call processing for the voice network architecture. The soft switch sites manage the gateway sites in a preferred embodiment, using a protocol such as the Internet Protocol Device Control (IPDC) protocol to request the set-up and tear-down of calls. The gateway sites originate and terminate calls between calling parties and called parties through the data network. The gateway sites include network access devices to provide access to network resources. The data network connects one or more of the soft switch sites to one or more of the gateway sites. The provisioning and network event component collects call events recorded at the soft switch sites. The network management component includes a network operations center (NOC) for centralized network management.

Voice over data telecommunications network architecture

Manual evaluation of translation quality is generally thought to be excessively time consuming and expensive. We explore a fast and inexpensive way of doing it using Amazon's Mechanical Turk to pay small sums to a large number of non-expert annotators. For $10 we redundantly recreate judgments from a WMT08 translation task. We find that when combined non-expert judgments have a high-level of agreement with the existing gold-standard judgments of machine translation quality, and correlate more strongly with expert judgments than Bleu does. We go on to show that Mechanical Turk can be used to calculate human-mediated translation edit rate (HTER), to conduct reading comprehension experiments with machine translation, and to create high quality reference translations.

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Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon's Mechanical Turk

The past decade has witnessed substantial progress towards the application of low-rate speech coders to civilian and military communications as well as computer-related voice applications. Central to this progress has been the development of new speech coders capable of producing high-quality speech at low data rates. Most of these coders incorporate mechanisms to: represent the spectral properties of speech, provide for speech waveform matching, and "optimize" the coder's performance for the human ear. A number of these coders have already been adopted in national and international cellular telephony standards. The objective of this paper is to provide a tutorial overview of speech coding methodologies with emphasis on those algorithms that are part of the recent low-rate standards for cellular communications. Although the emphasis is on the new low-rate coders, we attempt to provide a comprehensive survey by covering some of the traditional methodologies as well. We feel that this approach will not only point out key references but will also provide valuable background to the beginner. The paper starts with a historical perspective and continues with a brief discussion on the speech properties and performance measures. We then proceed with descriptions of waveform coders, sinusoidal transform coders, linear predictive vocoders, and analysis-by-synthesis linear predictive coders. Finally, we present concluding remarks followed by a discussion of opportunities for future research. >

Speech coding: a tutorial review

This paper evaluates the translation quality of machine translation systems for 8 language pairs: translating French, German, Spanish, and Czech to English and back. We carried out an extensive human evaluation which allowed us not only to rank the different MT systems, but also to perform higher-level analysis of the evaluation process. We measured timing and intra- and inter-annotator agreement for three types of subjective evaluation. We measured the correlation of automatic evaluation metrics with human judgments. This meta-evaluation reveals surprising facts about the most commonly used methodologies.

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(Meta-) Evaluation of Machine Translation

The intelligibility of speech transmitted through low-rate coders is severely degraded when high levels of acoustic noise are present in the acoustic environment. Recent advances in nonacoustic sensors, including microwave radar, skin vibration, and bone conduction sensors, provide the exciting possibility of both glottal excitation and, more generally, vocal tract measurements that are relatively immune to acoustic disturbances and can supplement the acoustic speech waveform. We are currently investigating methods of combining the output of these sensors for use in low-rate encoding according to their capability in representing specific speech characteristics in different frequency bands. Nonacoustic sensors have the ability to reveal certain speech attributes lost in the noisy acoustic signal; for example, low-energy consonant voice bars, nasality, and glottalized excitation. By fusing nonacoustic low-frequency and pitch content with acoustic-microphone content, we have achieved significant intelligibility performance gains using the DRT across a variety of environments over the government standard 2400-bps MELPe coder. By fusing quantized high-band 4-to-8-kHz speech, requiring only an additional 116 bps, we obtain further DRT performance gains by exploiting the ear's insensitivity to fine spectral detail in this frequency region.

Exploiting Nonacoustic Sensors for Speech Encoding

Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. This content is a rich data source for constructing and analyzing social networks, but its enormity and unstructured nature also present multiple challenges. Work at Lincoln Laboratory is addressing the problems in constructing networks from unstructured data, analyzing the community structure of a network, and inferring information from networks. Graph analytics have proven to be valuable tools in solving these challenges. Through the use of these tools, Laboratory researchers have achieved promising results on real-world data. A sampling of these results are presented in this article.

Social Network Analysis with Content and Graphs

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.

Modeling and detection techniques for Counter-Terror Social Network Analysis and Intent Recognition

We present highlights from three experiments that test the readability of current state-of-the art system output from: (1) an automated English speech-to-text (SST) system; (2) a text-based Arabic-to-English machine translation (MT) system; and (3) an audio-based Arabic-to-English MT process. We measure readability in terms of reaction time and passage comprehension in each case, applying standard psycholinguistic testing procedures and a modified version of the standard defense language proficiency test for Arabic called the DLPT*. We learned that: (1) subjects are slowed down by about 25% when reading system STT output; (2) text-based MT systems enable an English speaker to pass Arabic Level 2 on the DLPT*; and (3) audio-based MT systems do not enable English speakers to pass Arabic Level 2. We intend for these generic measures of readability to predict performance of more application-specific tasks.

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Measuring human readability of machine generated text: three case studies in speech recognition and machine translation

An Experimental Integrated Switched Network (EISN) has been developed to provide a system-level testbed for the evaluation of advanced communications networking techniques, including survivable network routing algorithms using a mix of transmission media, for application in the Defense Switched Network (DSN). EISN includes five CONUS sites linked by a wideband demand-assigned satellite channel and by dialed-up terrestrial trunks for alternate satellite/terrestrial routing experiments. Experiments to date have validated techniques for integration of circuit-switched terrestrial systems with the demand-assigned satellite system, and for the establishment of alternate routes over satellite and terrestrial paths. Currently, candidate routing algorithms for application in the DSN are being implemented and tested using external routing/controller processors attached to digital circuit switches at EISN sites. In addition, EISN is also being used to support data communication experiments using DoD standard data protocols in a combined satellite/ terrestrial network environment. Work is ongoing both in system experiments and in testbed developments to include additional capabilities. This paper represents a description and status report on both the testbed and the experimental efforts.

Clifford J. Weinstein

Papers

Exploiting Nonacoustic Sensors for Speech Encoding

Social Network Analysis with Content and Graphs

Modeling and detection techniques for Counter-Terror Social Network Analysis and Intent Recognition

Measuring human readability of machine generated text: three case studies in speech recognition and machine translation

The Experimental Integrated Switched Network - a System-Level Network Test Facility