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The importance of speech and language technologies continues to grow as information, and information needs, pervade every aspect of our lives and every corner of the globe. Speech and language technologies are used to automatically transcribe, analyze, route and extract information from highvolume streams of spoken and written information. Equally important, these technologies are also used to create natural and efficient interfaces between people and machines.
The role of mathematics and statistics in speech and language technologies cannot be overestimated. The rate at which we continue to accumulate speech and language training data is far greater than the rate at which our understanding of the speech and language phenomena grows. As a result, the relative advantage of data driven techniques continues to grow with time, and with it, the importance of mathematical and statistical methods that make use of such data.
In this volume, we have compiled papers representing some original contributions presented by participants during the two workshops. More information about the various workshop presentations and discussions can be found online, at http://jwww.ima.umn.edu/multimediaj. In this volume, chapters are organized starting with four contributions related to language processing, moving from more general work to specific advances in structure and topic representations in language modeling. The fifth paper on prosody modeling provides a nice transition, since prosody can be seen as an important link between acoustic and language modeling. The next five papers relate primarily to acoustic modeling, starting with work that is motivated by speech production models and acoustic-phonetic studies, and then moving toward more general work on new models. The book concludes with two contributions from the statistics community that we believe will impact speech and language processing in the future.
Probability and statistics in computational linguistics, a brief review
Three issues in modern language modeling
Stochastic analysis of Structured Language Modeling
Latent semantic language modeling for speech recognition
Prosody modeling for automatic speech recognition and understanding
Switching dynamic system models for speech articulation and acoustics
Segmental HMMS: Modeling dynamics and underlying structure in speech
Modelling graph-based observation spaces for segment-based speech recognition
Towards robust and adaptive speech recognition models
Graphical models and automatic speech recognition
An introduction to Markov chain Monte Carlo methods
Semiparametric filtering in speech processing