Robust Parsing of Spoken Dialogue Using Contextual Knowledge and Recognition Probabilities

In this paper we describe the linguistic processor of a spoken dialogue system. The parser receives a word graph from the recognition module as its input. Its task is to find the best path through the graph. If no complete solution can be found, a robust mechanism for selecting multiple partial results is applied. We show how the information content rate of the results can be improved if the selection is based on an integrated quality score combining word recognition scores and context-dependent semantic predictions. Results of parsing word graphs with and without predictions are reported.Comment: 4 pages, LaTex source, 3 PostScript figures, uses epsf.sty and ETRW.sty, to appear in Proceedings of ESCA Workshop on Spoken Dialogue Systems, Denmark, May 30-June

Parsing of Spoken Language under Time Constraints

Spoken language applications in natural dialogue settings place serious requirements on the choice of processing architecture. Especially under adverse phonetic and acoustic conditions parsing procedures have to be developed which do not only analyse the incoming speech in a time-synchroneous and incremental manner, but which are able to schedule their resources according to the varying conditions of the recognition process. Depending on the actual degree of local ambiguity the parser has to select among the available constraints in order to narrow down the search space with as little effort as possible. A parsing approach based on constraint satisfaction techniques is discussed. It provides important characteristics of the desired real-time behaviour and attempts to mimic some of the attention focussing capabilities of the human speech comprehension mechanism.Comment: 19 pages, LaTe

An investigation of grammar design in natural-language speech-recognition.

With the growing interest and demand for human-machine interaction, much work concerning speech-recognition has been carried out over the past three decades. Although a variety of approaches have been proposed to address speech-recognition issues, such as stochastic (statistical) techniques, grammar-based techniques, techniques integrated with linguistic features, and other approaches, recognition accuracy and robustness remain among the major problems that need to be addressed. At the state of the art, most commercial speech products are constructed using grammar-based speech-recognition technology. In this thesis, we investigate a number of features involved in grammar design in natural-language speech-recognition technology. We hypothesize that: with the same domain, a semantic grammar, which directly encodes some semantic constraints into the recognition grammar, achieves better accuracy, but less robustness; a syntactic grammar defines a language with a larger size, thereby it has better robustness, but less accuracy; a word-sequence grammar, which includes neither semantics nor syntax, defines the largest language, therefore, is the most robust, but has very poor recognition accuracy. In this Master\u27s thesis, we claim that proper grammar design can achieve the appropriate compromise between recognition accuracy and robustness. The thesis has been proven by experiments using the IBM Voice-Server SDK, which consists of a VoiceXML browser, IBM ViaVoice Speech Recognition and Text-To-Speech (TTS) engines, sample applications, and other tools for developing and testing VoiceXML applications. The experimental grammars are written in the Java Speech Grammar Format (JSGF), and the testing applications are written in VoiceXML. The tentative experimental results suggest that grammar design is a good area for further study. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .S555. Source: Masters Abstracts International, Volume: 43-01, page: 0244. Adviser: Richard A. Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2004

An investigation of the electrolytic plasma oxidation process for corrosion protection of pure magnesium and magnesium alloy AM50.

In this study, silicate and phosphate EPO coatings were produced on pure magnesium using an AC power source. It was found that the silicate coatings possess good wear resistance, while the phosphate coatings provide better corrosion protection. A Design of Experiment (DOE) technique, the Taguchi method, was used to systematically investigate the effect of the EPO process parameters on the corrosion protection properties of a coated magnesium alloy AM50 using a DC power. The experimental design consisted of four factors (treatment time, current density, and KOH and NaAlO2 concentrations), with three levels of each factor. Potentiodynamic polarization measurements were conducted to determine the corrosion resistance of the coated samples. The optimized processing parameters are 12 minutes, 12 mA/cm2 current density, 0.9 g/l KOH, 15.0 g/l NaAlO2. The results of the percentage contribution of each factor determined by the analysis of variance (ANOVA) imply that the KOH concentration is the most significant factor affecting the corrosion resistance of the coatings, while treatment time is a major factor affecting the thickness of the coatings. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M323. Source: Masters Abstracts International, Volume: 44-03, page: 1479. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed