A Comparison of Front-Ends for Bitstream-Based ASR over IP

Automatic speech recognition (ASR) is called to play a relevant role in the provision of spoken interfaces for IP-based applications. However, as a consequence of the transit of the speech signal over these particular networks, ASR systems need to face two new challenges: the impoverishment of the speech quality due to the compression needed to fit the channel capacity and the inevitable occurrence of packet losses. In this framework, bitstream-based approaches that obtain the ASR feature vectors directly from the coded bitstream, avoiding the speech decoding process, have been proposed ([S.H. Choi, H.K. Kim, H.S. Lee, Speech recognition using quantized LSP parameters and their transformations in digital communications, Speech Commun. 30 (4) (2000) 223–233. A. Gallardo-Antolín, C. Pelàez-Moreno, F. Díaz-de-María, Recognizing GSM digital speech, IEEE Trans. Speech Audio Process., to appear. H.K. Kim, R.V. Cox, R.C. Rose, Performance improvement of a bitstream-based front-end for wireless speech recognition in adverse environments, IEEE Trans. Speech Audio Process. 10 (8) (2002) 591–604. C. Peláez-Moreno, A. Gallardo-Antolín, F. Díaz-de-María, Recognizing voice over IP networks: a robust front-end for speech recognition on the WWW, IEEE Trans. Multimedia 3(2) (2001) 209–218], among others) to improve the robustness of ASR systems. LSP (Line Spectral Pairs) are the preferred set of parameters for the description of the speech spectral envelope in most of the modern speech coders. Nevertheless, LSP have proved to be unsuitable for ASR, and they must be transformed into cepstrum-type parameters. In this paper we comparatively evaluate the robustness of the most significant LSP to cepstrum transformations in a simulated VoIP (voice over IP) environment which includes two of the most popular codecs used in that network (G.723.1 and G.729) and several network conditions. In particular, we compare ‘pseudocepstrum’ [H.K. Kim, S.H. Choi, H.S. Lee, On approximating Line Spectral Frequencies to LPC cepstral coefficients, IEEE Trans. Speech Audio Process. 8 (2) (2000) 195–199], an approximated but straightforward transformation of LSP into LP cepstral coefficients, with a more computationally demanding but exact one. Our results show that pseudocepstrum is preferable when network conditions are good or computational resources low, while the exact procedure is recommended when network conditions become more adverse.Publicad

Graded quantization for multiple description coding of compressive measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics

Regular Topologies for Gigabit Wide-Area Networks

In general terms, this project aimed at the analysis and design of techniques for very high-speed networking. The formal objectives of the project were to: (1) Identify switch and network technologies for wide-area networks that interconnect a large number of users and can provide individual data paths at gigabit/s rates; (2) Quantitatively evaluate and compare existing and proposed architectures and protocols, identify their strength and growth potentials, and ascertain the compatibility of competing technologies; and (3) Propose new approaches to existing architectures and protocols, and identify opportunities for research to overcome deficiencies and enhance performance. The project was organized into two parts: 1. The design, analysis, and specification of techniques and protocols for very-high-speed network environments. In this part, SRI has focused on several key high-speed networking areas, including Forward Error Control (FEC) for high-speed networks in which data distortion is the result of packet loss, and the distribution of broadband, real-time traffic in multiple user sessions. 2. Congestion Avoidance Testbed Experiment (CATE). This part of the project was done within the framework of the DARTnet experimental T1 national network. The aim of the work was to advance the state of the art in benchmarking DARTnet's performance and traffic control by developing support tools for network experimentation, by designing benchmarks that allow various algorithms to be meaningfully compared, and by investigating new queueing techniques that better satisfy the needs of best-effort and reserved-resource traffic. This document is the final technical report describing the results obtained by SRI under this project. The report consists of three volumes: Volume 1 contains a technical description of the network techniques developed by SRI in the areas of FEC and multicast of real-time traffic. Volume 2 describes the work performed under CATE. Volume 3 contains the source code of all software developed under CATE

Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.Comment: 28 pages, 14 figure

Error Resilient Video Coding Using Bitstream Syntax And Iterative Microscopy Image Segmentation

There has been a dramatic increase in the amount of video traffic over the Internet in past several years. For applications like real-time video streaming and video conferencing, retransmission of lost packets is often not permitted. Popular video coding standards such as H.26x and VPx make use of spatial-temporal correlations for compression, typically making compressed bitstreams vulnerable to errors. We propose several adaptive spatial-temporal error concealment approaches for subsampling-based multiple description video coding. These adaptive methods are based on motion and mode information extracted from the H.26x video bitstreams. We also present an error resilience method using data duplication in VPx video bitstreams. A recent challenge in image processing is the analysis of biomedical images acquired using optical microscopy. Due to the size and complexity of the images, automated segmentation methods are required to obtain quantitative, objective and reproducible measurements of biological entities. In this thesis, we present two techniques for microscopy image analysis. Our first method, “Jelly Filling” is intended to provide 3D segmentation of biological images that contain incompleteness in dye labeling. Intuitively, this method is based on filling disjoint regions of an image with jelly-like fluids to iteratively refine segments that represent separable biological entities. Our second method selectively uses a shape-based function optimization approach and a 2D marked point process simulation, to quantify nuclei by their locations and sizes. Experimental results exhibit that our proposed methods are effective in addressing the aforementioned challenges

Progressively communicating rich telemetry from autonomous underwater vehicles via relays

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2012As analysis of imagery and environmental data plays a greater role in mission construction and execution, there is an increasing need for autonomous marine vehicles to transmit this data to the surface. Without access to the data acquired by a vehicle, surface operators cannot fully understand the state of the mission. Communicating imagery and high-resolution sensor readings to surface observers remains a significant challenge – as a result, current telemetry from free-roaming autonomous marine vehicles remains limited to ‘heartbeat’ status messages, with minimal scientific data available until after recovery. Increasing the challenge, longdistance communication may require relaying data across multiple acoustic hops between vehicles, yet fixed infrastructure is not always appropriate or possible. In this thesis I present an analysis of the unique considerations facing telemetry systems for free-roaming Autonomous Underwater Vehicles (AUVs) used in exploration. These considerations include high-cost vehicle nodes with persistent storage and significant computation capabilities, combined with human surface operators monitoring each node. I then propose mechanisms for interactive, progressive communication of data across multiple acoustic hops. These mechanisms include wavelet-based embedded coding methods, and a novel image compression scheme based on texture classification and synthesis. The specific characteristics of underwater communication channels, including high latency, intermittent communication, the lack of instantaneous end-to-end connectivity, and a broadcast medium, inform these proposals. Human feedback is incorporated by allowing operators to identify segments of data thatwarrant higher quality refinement, ensuring efficient use of limited throughput. I then analyze the performance of these mechanisms relative to current practices. Finally, I present CAPTURE, a telemetry architecture that builds on this analysis. CAPTURE draws on advances in compression and delay tolerant networking to enable progressive transmission of scientific data, including imagery, across multiple acoustic hops. In concert with a physical layer, CAPTURE provides an endto- end networking solution for communicating science data from autonomous marine vehicles. Automatically selected imagery, sonar, and time-series sensor data are progressively transmitted across multiple hops to surface operators. Human operators can request arbitrarily high-quality refinement of any resource, up to an error-free reconstruction. The components of this system are then demonstrated through three field trials in diverse environments on SeaBED, OceanServer and Bluefin AUVs, each in different software architectures.Thanks to the National Science Foundation, and the National Oceanic and Atmospheric Administration for their funding of my education and this work