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Full-Text Articles in Computational Linguistics
Introducing Señal, A Computational Tool For The Linguistic Analysis Of Spanish L2 Compositions, Falcon Restrepo-Ramos
Introducing Señal, A Computational Tool For The Linguistic Analysis Of Spanish L2 Compositions, Falcon Restrepo-Ramos
World Languages & Cultures Department Publications
SEÑAL is a modular program that automatizes and facilitates the assessment of Spanish L2 compositions. The tool can extract syntactic and lexical information, while also assessing grammar, and Spanish L2 proficiency. A computational tool that can process written learners’ corpora and extract measures of language development has enormous practical value in Spanish and modern language departments alike.
A Wavelet Transform Module For A Speech Recognition Virtual Machine, Euisung Kim
A Wavelet Transform Module For A Speech Recognition Virtual Machine, Euisung Kim
All Graduate Theses, Dissertations, and Other Capstone Projects
This work explores the trade-offs between time and frequency information during the feature extraction process of an automatic speech recognition (ASR) system using wavelet transform (WT) features instead of Mel-frequency cepstral coefficients (MFCCs) and the benefits of combining the WTs and the MFCCs as inputs to an ASR system. A virtual machine from the Speech Recognition Virtual Kitchen resource (www.speechkitchen.org) is used as the context for implementing a wavelet signal processing module in a speech recognition system. Contributions include a comparison of MFCCs and WT features on small and large vocabulary tasks, application of combined MFCC and WT features on …
Dialog Act Modeling For Automatic Tagging And Recognition Of Conversational Speech, Andreas Stolcke, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, Daniel Jurafsky, Paul Taylor, Rachel Martin, Carol Van Ess-Dykema, Marie Meteer
Dialog Act Modeling For Automatic Tagging And Recognition Of Conversational Speech, Andreas Stolcke, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, Daniel Jurafsky, Paul Taylor, Rachel Martin, Carol Van Ess-Dykema, Marie Meteer
Integrated Engineering Department Publications
We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Back channel, 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 …