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Incorporating nonverbal features into multimodal models of human-to-human communication
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In: Theses and Dissertations Available from ProQuest (2008)
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Structural event detection for rich transcription of speech
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In: Theses and Dissertations Available from ProQuest (2004)
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The Effect of Pruning and Compression on Graphical Representations of the Output of a Speech Recognizer
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In: Electrical and Computer Engineering Faculty Research and Publications (2003)
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Statistical parsing and language modeling based on constraint dependency grammar
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In: Theses and Dissertations Available from ProQuest (2003)
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Investigating Probabilistic Constraint Dependency Grammars in Language Modeling
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In: Department of Electrical and Computer Engineering Technical Reports (2001)
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Familiarity and Pronounceability of Nouns and Names
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Suprenant, Aimeé M.; Hura, Susan L.; Harper, Mary P.; Jamieson, Leah H.; Long, Glenis; Thede, Scott M.; Rout, Ayasakanta; Hseuh, Tsung-Hsiang; Hockema, Stephen A.; Johnson, Michael T.; Srinivasan, Pramila N.; White, Christopher M.; Laflen, J. Brandon
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In: Electrical and Computer Engineering Faculty Research and Publications (1999)
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Abstract:
Ratings of familiarity and pronounceability were obtained from a random sample of 199 surnames (selected from over 80,000 entries in the Purdue University phone book) and 199 nouns (from the Kučera-Francis, 1967, word database). The distributions of ratings for nouns versus names are substantially different: Nouns were rated as more familiar and easier to pronounce than surnames. Frequency and familiarity were more closely related in the proper name pool than the word pool, although both correlations were modest. Ratings of familiarity and pronounceability were highly related for both groups. A production experiment showed that rated pronounceability was highly related to the time taken to produce a name. These data confirm the common belief that there are differences in the statistical and distributional properties of words as compared to proper names. The value of using frequency and the ratings of familiarity and pronounceability for predicting variations in actual pronunciations of words and names are discussed.
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Keyword:
Computer Engineering; Electrical and Computer Engineering; Engineering
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URL: https://epublications.marquette.edu/electric_fac/35 https://doi.org/10.3758/BF03200740
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Parsing and tagging sentences containing lexically ambiguous and unknown tokens
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In: Theses and Dissertations Available from ProQuest (1999)
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Prosodic disambiguation in automatic speech understanding of Thai
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In: Theses and Dissertations Available from ProQuest (1995)
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Improving hidden Markov models for speech recognition
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In: Theses and Dissertations Available from ProQuest (1995)
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PARSEC: A Constraint-Based Parser for Spoken Language Processing
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In: Department of Electrical and Computer Engineering Technical Reports (1993)
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