Mathematical Foundations of Speech and Language Processing
By: edited by Mark Johnson, Sanjeev P. Khudanpur, Mari Ostendorf, Roni Rosenfeld
Contributors: Khudanpur, Sanjeev P.; Ostendorf, Mari; Rosenfeld, Roni
Series: The IMA Volumes in Mathematics and its Applications -- 138; IMA volumes in mathematics and its applications -- 138.
Publisher: Springer New York
Published: 2004
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 289
ISBN-13: 9781461264842; 9781441990174
Subjects: Optical pattern recognition; Mathematics; Translators (Computer programs)
Description: Speech and language technologies continue to grow in importance as they are used to create natural and efficient interfaces between people and machines, and to automatically transcribe, extract, analyze, and route information from high-volume streams of spoken and written information. The workshops on Mathematical Foundations of Speech Processing and Natural Language Modeling were held in the Fall of 2000 at the University of Minnesota's NSF-sponsored Institute for Mathematics and Its Applications, as part of a "Mathematics in Multimedia" year-long program. Each workshop brought together researchers in the respective technologies on one hand, and mathematicians and statisticians on the other hand, for an intensive week of cross-fertilization. There is a long history of benefit from introducing mathematical techniques and ideas to speech and language technologies. Examples include the source-channel paradigm, hidden Markov models, decision trees, exponential models and formal languages theory. It is likely that new mathematical techniques, or novel applications of existing techniques, will once again prove pivotal for moving the field forward. This volume consists of original contributions presented by participants during the two workshops. Topics include language modeling, prosody, acoustic-phonetic modeling, and statistical methodology.
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Table of contents: title: Probability and statistics in computational linguistics, a brief review; title: Three issues in modern language modeling; title: Stochastic analysis of Structured Language Modeling; title: Latent semantic language modeling for speech recognition; title: Prosody modeling for automatic speech recognition and understanding; title: Switching dynamic system models for speech articulation and acoustics; title: Segmental HMMS: Modeling dynamics and underlying structure in speech; title: Modelling graph-based observation spaces for segment-based speech recognition; title: Towards robust and adaptive speech recognition models; title: Graphical models and automatic speech recognition; title: An introduction to Markov chain Monte Carlo method; title: Semiparametric filtering in speech processing.