27960177
DISPEL: A Run-Time Debugging Language
Dispel is a language designed to aid communication between an interactive user and a run-time, symbolic debugging system. Important attributes of Dispel are that it provides a small set of primitive debugging actions, most traditional debugging aids (such as variable traces and postmortem dumps) are written in terms of these primitives as debugging routines, and Dispel serves both as an interactive debugging command language and as a special-purpose programming language. The syntax and semantics of Dispel are explained and examples of Dispel commands and routines are presented.
39453826
A software debugging glossary
This glossary contains 291 definitions of terms dealing with the debugging of computer software. The list includes numerous synonyms, as well as the proper names of debugging systems described in the open literature. Terms and definitions have been obtained from various sources: the software-engineering literature, other software-engineering glossaries, and individual contributions.
18721179
Some requirements for architectural support of software debugging
Architectural support of high-level, symbolic debugging is described at three levels of abstraction: the user's view of desired debugging functionality, the debugger implementor's view of architectural requirements that support the functionality, and the computer architect's view of architectural features or attributes that implement the requirements. References are made where possible to computing systems that meet the requirements. The paper is written from the viewpoint of debugger implementors, and is addressed primarily to computer architects.
2640755
Translator Design to Support Run-time Debugging
32099474
Techniques to aid global search in engineering design
2234445
Attention Shifting for Parsing Speech
We present a technique that improves the efficiency of word-lattice parsing as used in speech recognition language modeling. Our technique applies a probabilistic parser iteratively where on each iteration it focuses on a different subset of the word-lattice. The parser's attention is shifted towards word-lattice subsets for which there are few or no syntactic analyses posited. This attention-shifting technique provides a six-times increase in speed (measured as the number of parser analyses evaluated) while performing equivalently when used as the first-stage of a multi-stage parsing-based language model.
1613345
Features and Agreement
This paper compares the consistency-based account of agreement phenomena in 'unification-based' grammars with an implication-based account based on a simple feature extension to Lambek Categorial Grammar (LCG). We show that the LCG treatment accounts for constructions that have been recognized as problematic for 'unification-based' treatments.
26304890
Speech signal enhancement through adaptive wavelet thresholding
This paper demonstrates the application of the Bionic Wavelet Transform (BWT), an adaptive wavelet transform derived from a non-linear auditory model of the cochlea, to the task of speech signal enhancement. Results, measured objectively by Signal-to-Noise ratio (SNR) and Segmental SNR (SSNR) and subjectively by Mean Opinion Score (MOS), are given for additive white Gaussian noise as well as four different types of realistic noise environments. Enhancement is accomplished through the use of thresholding on the adapted BWT coefficients, and the results are compared to a variety of speech enhancement techniques, including Ephraim Malah filtering, iterative Wiener filtering, and spectral subtraction, as well as to wavelet denoising based on a perceptually scaled wavelet packet transform decomposition. Overall results indicate that SNR and SSNR improvements for the proposed approach are comparable to those of the Ephraim Malah filter, with BWT enhancement giving the best results of all methods for the noisiest (-10db and -5db input SNR) conditions. Subjective measurements using MOS surveys across a variety of 0db SNR noise conditions indicate enhancement quality competitive with but still lower than results for Ephraim Malah filtering and iterative Wiener filtering, but higher than the perceptually scaled wavelet method.
14001621
Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm
This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random fields (CRFs). The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. However, using the feature set output from the perceptron algorithm (initialized with their weights), CRF training provides an additional 0.5% reduction in word error rate, for a total 1.8% absolute reduction from the baseline of 39.2%.