Artificial intelligence, Natural language processing, Speech recognition, Linguistics and Word are his primary areas of study. Richard Sproat has researched Artificial intelligence in several fields, including Pronunciation, Intonation and Index. His research in Natural language processing is mostly concerned with Natural language.
His Natural language research is multidisciplinary, incorporating perspectives in Programming language, Software, Compiler and Interface. His work carried out in the field of Speech recognition brings together such families of science as Segmentation, Named entity, Search engine indexing and Transliteration. Many of his studies involve connections with topics such as Cognitive science and Linguistics.
His primary areas of study are Artificial intelligence, Natural language processing, Linguistics, Speech recognition and Speech synthesis. His Artificial intelligence research incorporates themes from Pronunciation and Set. His Set study combines topics in areas such as Polysemy and Representation.
His Natural language processing research integrates issues from Text to speech synthesis and Grammar. His work on Linguistics deals in particular with Phonology, Mandarin Chinese, Symbol, Syllable and Morpheme. His Speech recognition study integrates concerns from other disciplines, such as Named entity and Hindi.
His primary scientific interests are in Artificial intelligence, Natural language processing, Text normalization, Speech recognition and Linguistics. The concepts of his Artificial intelligence study are interwoven with issues in Simple and Estimator. He performs multidisciplinary study in Natural language processing and Task in his work.
His Text normalization research includes elements of Parsing, Encoder, Bengali and Semiotics. His Speech recognition research integrates issues from Principle of maximum entropy and Spoken language processing. The various areas that Richard Sproat examines in his Linguistics study include Word list and Set.
Richard Sproat focuses on Speech recognition, Text normalization, Artificial intelligence, Natural language processing and Normalization. The study incorporates disciplines such as Artificial neural network, Recurrent neural network and Domain in addition to Speech recognition. His work carried out in the field of Recurrent neural network brings together such families of science as Crowdsourcing, Hindi, Bengali and Leverage.
His Text normalization research is multidisciplinary, incorporating elements of Component, Protocol, Rule-based machine translation and Code. His Principle of maximum entropy research extends to Natural language processing, which is thematically connected. His research investigates the connection between Normalization and topics such as Machine learning that intersect with issues in Training set.
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Emotions from Text: Machine Learning for Text-based Emotion Prediction
Cecilia Ovesdotter Alm;Dan Roth;Richard Sproat.
empirical methods in natural language processing (2005)
Allophonic variation in English /l/ and its implications for phonetic implementation
Richard Sproat;Osamu Fujimura.
Journal of Phonetics (1993)
WordsEye: an automatic text-to-scene conversion system
Bob Coyne;Richard Sproat.
international conference on computer graphics and interactive techniques (2001)
Morphology and computation
Richard William Sproat.
On deriving the lexicon
Richard William Sproat.
A stochastic finite-state word-segmentation algorithm for Chinese
Richard Sproat;William Gale;Chilin Shih;Nancy Chang.
Computational Linguistics (1996)
Normalization of non-standard words
Richard Sproat;Alan W. Black;Stanley Chen;Shankar Kumar.
Computer Speech & Language (2001)
The Cross-Linguistic Distribution of Adjective Ordering Restrictions
Richard Sproat;Chilin Shih.
Multilingual Text-to-Speech Synthesis
Richard W. Sproat.
Lattice-Based Search for Spoken Utterance Retrieval
Murat Saraclar;Richard Sproat.
north american chapter of the association for computational linguistics (2004)
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