A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition

This paper presents a method for reconstructing unreliable spectral components of speech signals using the statistical distributions of the clean components. Our goal is to model the temporal patterns in speech signal and take advantage of correlations between speech features in both time and frequency domain simultaneously. In this approach, a hidden Markov model (HMM) is first trained on clean speech data to model the temporal patterns which appear in the sequences of the spectral components. Using this model and according to the probabilities of occurring noisy spectral component at each states, a probability distributions for noisy components are estimated. Then, by applying maximum a posteriori (MAP) estimation on the mentioned distributions, the final estimations of the unreliable spectral components are obtained. The proposed method is compared to a common missing feature method which is based on the probabilistic clustering of the feature vectors and also to a state of the art method based on sparse reconstruction. The experimental results exhibits significant improvement in recognition accuracy over a noise polluted Persian corpus.     

Textual image compression at low bit rates based on region-of-interest coding

In this paper, we deal with those applications of textual image compression where high compression ratio and maintaining or improving the visual quality and readability of the compressed images are of main concern. In textual images, most of the information exists in the edge regions; therefore, the compression problem can be studied in the framework of region-of-interest (ROI) coding. In this paper, the Set Partitioning in Hierarchical Trees (SPIHT) coder is used in the framework of ROI coding along with some image enhancement techniques in order to remove the leakage effect which occurs in the wavelet-based low-bit-rate compression. We evaluated the compression performance of the proposed method with respect to some qualitative and quantitative measures. The qualitative measures include the averaged mean opinion scores (MOS) curve along with demonstrating some outputs in different conditions. The quantitative measures include two proposed modified PSNR measures and the conventional one. Comparing the results of the proposed method with those of three conventional approaches, DjVu, JPEG2000, and SPIHT coding, showed that the proposed compression method considerably outperformed the others especially from the qualitative aspect. The proposed method improved the MOS by 20 and 30 %, in average, for high- and low-contrast textual images, respectively. In terms of the modified and conventional PSNR measures, the proposed method outperformed DjVu and JPEG2000 up to 0.4 dB for high-contrast textual images at low bit rates. In addition, compressing the high contrast images using the proposed ROI technique, compared to without using this technique, improved the average textual PSNR measure up to 0.5 dB, at low bit rates.     

What can possibly go wrong? Anticipatory work in space operations

This paper explores how different forms of anticipatory work contribute to reliability in high-risk space operations. It is based on ethnographic field work, participant observation and interviews supplemented with video recordings from a control room responsible for operating a microgravity greenhouse at the International Space Station (ISS). Drawing on examples from different stages of a biological experiment on the ISS, we demonstrate how engineers, researchers and technicians work to anticipate and proactively mitigate possible problems. Space research is expensive and risky. The experiments are planned over the course of many years by a globally distributed network of organizations. Owing to the inaccessibility of the ISS, every trivial detail that could possibly cause a problem is subject to scrutiny. We discuss what we label anticipatory work: practices constituted of an entanglement of cognitive, social and technical elements involved in anticipating and proactively mitigating everything that might go wrong. We show how the nature of anticipatory work changes between planning and the operational phases of an experiment. In the planning phase, operators inscribe their anticipation into technology and procedures. In the operational phase, we show how troubleshooting involves the ability to look ahead in the evolving temporal trajectory of the ISS operations and to juggle pre-planned fixes along these trajectories. A key objective of this paper is to illustrate how anticipation is shared between humans and different forms of technology. Moreover, it illustrates the importance of including considerations of temporality in safety and reliability research.     

Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.     

Cross level semantic similarity: an evaluation framework for universal measures of similarity

Semantic similarity has typically been measured across items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of linguistic item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of linguistic item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method’s quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of items.     

Safe Autonomous Agent Formation Operations Via Obstacle Collision Avoidance

In this paper, we consider the problem of flocking and shape‐orientation control of multi‐agent systems with inter‐agent and obstacle collision avoidance. We first consider the problem of forcing a set of autonomous agents to form a desired formation shape and orientation while avoiding inter‐agent collision and collision with convex obstacles, and following a trajectory known to only one of the agents, namely the leader of the formation. Then we build upon the solution given to this problem and solve the problem of guaranteeing obstacle collision avoidance by changing the size and the orientation of the formation. Changing the size and the orientation of the formation is helpful when the agents want to go through a narrow passage while the existing size or orientation of the formation does not allow this. We also propose collision avoidance algorithms that temporarily change the shape of the formation to avoid collision with stationary or moving nonconvex obstacles. Simulation results are presented to show the performance of the proposed control laws.     

Sound source localization using compressive sensing-based feature extraction and spatial sparsity

In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases.     

An enhanced motion estimation approach using a genetic trail bounded approximation for H.264/AVC codecs

Genetic algorithm-based motion estimation schemes play a significant role in improving the results of H.264/AVC standardization efforts when addressing conversational and non-conversational video applications. In this paper, we present a robust motion estimation scheme that uses a noble genetic trail bounded approximation (GTBA) approach to speed up the encoding process of H.264/AVC video compression and to reduce the number of bits required to code frame. The proposed algorithm is utilized to enhance the fitness function strength by integrating trail information of motion vector and sum of absolute difference (SAD) information into a fitness function. Experimental results reveal that the proposed GTBA resolves conflict obstacles with respect to both the number of bits required to code frames and the execution time for estimation.