AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Unsupervised and active learning in automatic speech recognition for call classification

Abstract: A key challenge in rapidly building spoken natural language dialog applications is minimizing the manual effort required in transcribing and labeling speech data. This task is not only expensive but also time consuming. We present a novel approach that aims at reducing the amount of manually transcribed in-domain data required for building automatic speech recognition (ASR) models in spoken language dialog systems. Our method is based on mining relevant text from various conversational systems and Web sites. An iterative process is employed where the performance of the models can be improved through both unsupervised and active learning of the ASR models. We have evaluated the robustness of our approach on a call classification task that has been selected from AT&T VoiceTone/sup SM/ customer care. Our results indicate that with unsupervised learning it is possible to achieve a call classification performance that is only 1.5% lower than the upper bound set when using all available in-domain transcribed data.

Efficient, DoS-resistant, secure key exchange for internet protocols

Abstract: We describe JFK, a new key exchange protocol, primarily designed for use in the IP Security Architecture. It is simple, efficient, and secure; we sketch a proof of the latter property. JFK also has a number of novel engineering parameters that permit a variety of trade-offs, most notably the ability to balance the need for perfect forward secrecy against susceptibility to denial-of-service attacks.

Challenges for MPLS in optical network restoration

Abstract: Previous standards proposals have focused on extending IP-based MPLS protocols to optical networks. These proposals have concentrated on provisioning optical connections. However, a key expectation of the optical network is that it will offer fast restoration capability, competitive in performance to SONET rings. To meet this expectation, there are key features that need to be added to the current proposals to extend MPLS for the optical network. This article discusses some of these key requirements.

Fast direction-aware proximity for graph mining

Abstract: In this paper we study asymmetric proximity measures on directed graphs, which quantify the relationships between two nodes or two groups of nodes. The measures are useful in several graph mining tasks, including clustering, link prediction and connection subgraph discovery. Our proximity measure is based on the conceptof escape probability. This way, we strive to summarize the multiple facets of nodes-proximity, while avoiding some of the pitfalls to which alternative proximity measures are susceptible. A unique feature of the measures is accounting for the underlying directional information. We put a special emphasis on computational efficiency, and develop fast solutions that are applicable in several settings. Our experimental study shows the usefulness of our proposed direction-aware proximity method for several applications, and that our algorithms achieve a significant speedup (up to 50,000x) over straight forward implementations.

MGV: a system for visualizing massive multidigraphs

Abstract: Describes MGV (Massive Graph Visualizer), an integrated visualization and exploration system for massive multidigraph navigation. It adheres to the visual information-seeking mantra: overview first, zoom and filter, then details on demand. MGV's only assumption is that the vertex set of the underlying digraph corresponds to the set of leaves of a pre-determined tree T. MGV builds an out-of-core graph hierarchy and provides mechanisms to plug in arbitrary visual representations for each graph hierarchy slice. Navigation from one level to another of the hierarchy corresponds to the implementation of a drill-down interface. In order to provide the user with navigation control and interactive response, MGV incorporates a number of visualization techniques like interactive pixel-oriented 2D and 3D maps, statistical displays, color maps, multi-linked views and a zoomable label-based interface. This makes the association of geographic information and graph data very natural. To automate the creation of the vertex set hierarchy for MGV, we use the notion of graph sketches. They can be thought of as visual indices that guide the navigation of a multigraph too large to fit on the available display. MGV follows the client-server paradigm and it is implemented in C and Java-3D. We highlight the main algorithmic and visualization techniques behind the tools and, along the way, point out several possible application scenarios. Our techniques are being applied to multigraphs defined on vertex sets with sizes ranging from 100 million to 250 million vertices.