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.

SCANMail: a voicemail interface that makes speech browsable, readable and searchable

Abstract: Increasing amounts of public, corporate, and private speech data are now available on-line. These are limited in their usefulness, however, by the lack of tools to permit their browsing and search. The goal of our research is to provide tools to overcome the inherent difficulties of speech access, by supporting visual scanning, search, and information extraction. We describe a novel principle for the design of UIs to speech data: What You See Is Almost What You Hear (WYSIAWYH). In WYSIAWYH, automatic speech recognition (ASR) generates a transcript of the speech data. The transcript is then used as a visual analogue to that underlying data. A graphical user interface allows users to visually scan, read, annotate and search these transcripts. Users can also use the transcript to access and play specific regions of the underlying message. We first summarize previous studies of voicemail usage that motivated the WYSIAWYH principle, and describe a voicemail UI, SCANMail, that embodies WYSIAWYH. We report on a laboratory experiment and a two-month field trial evaluation. SCANMail outperformed a state of the art voicemail system on core voicemail tasks. This was attributable to SCANMail's support for visual scanning, search and information extraction. While the ASR transcripts contain errors, they nevertheless improve the efficiency of voicemail processing. Transcripts either provide enough information for users to extract key points or to navigate to important regions of the underlying speech, which they can then play directly

Generating a privacy footprint on the internet

Abstract: As a follow up to characterizing traffic deemed as unwanted by Web clients such as advertisements, we examine how information related to individual users is aggregated as a result of browsing seemingly unrelated Web sites. We examine the privacy diffusion on the Internet, hidden transactions, and the potential for a few sites to be able to construct a profile of individual users. We define and generate a privacy footprint allowing us to assess and compare the diffusion of privacy information across a wide variety of sites. We examine the effectiveness of existing and new techniques to reduce this diffusion. Our results show that the size of the privacy footprint is a legitimate cause for concern across the sets of sites that we study.

Phase noise and linewidth in an InGaAsP DFB laser

Abstract: Measurements and calculations of interferometrically demodulated phase noise in an InGaAsP DFB laser are reported. The results led to a novel method of laser linewidth measurement. The effect of this noise on a DPSK coherent system is considered.

Coding approaches for multiple antenna transmission in fast fading and OFDM

Abstract: Multiple-antenna channel coding for orthogonal frequency-division multiplexing (OFDM) transmission over dispersive channels is reconsidered because with frequency interleaving, the effective channel characteristic across subcarriers is rather fast fading. The channel does not comply with the quasistatic model widely assumed for space-time trellis codes (STCs). For that reason, we first study the ideal fast-fading multiple transmit and receive antenna channel and then compare the performance of STCs with that of bit-interleaved coded modulation in fast fading. Mutual information of the ergodic channel is evaluated for numerous modulation scenarios, and capacity comparisons generate guidelines on how to jointly adjust coding rate and modulation cardinality. Bit-based coding offers large flexibility in rate adaptation, and simulation results show that it outperforms STCs in ideal fast fading and, finally, in a realistic OFDM application as well.

Fusing data with correlations

Abstract: Many applications rely on Web data and extraction systems to accomplish knowledge-driven tasks. Web information is not curated, so many sources provide inaccurate, or conflicting information. Moreover, extraction systems introduce additional noise to the data. We wish to automatically distinguish correct data and erroneous data for creating a cleaner set of integrated data. Previous work has shown that a naive voting strategy that trusts data provided by the majority or at least a certain number of sources may not work well in the presence of copying between the sources. However, correlation between sources can be much broader than copying: sources may provide data from complementary domains (negative correlation), extractors may focus on different types of information (negative correlation), and extractors may apply common rules in extraction (positive correlation, without copying). In this paper we present novel techniques modeling correlations between sources and applying it in truth finding. We provide a comprehensive evaluation of our approach on three real-world datasets with different characteristics, as well as on synthetic data, showing that our algorithms outperform the existing state-of-the-art techniques.