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Resourceful at Any Size: A Predictive Methodology Using Linguistic Corpus Metrics for Multi-Source Training in Neural Dependency Parsing
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ASR and Human Recognition Errors: Predictability and Lexical Factors
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The Language of Law: An Analysis of Gender and Turn-Taking in U.S. Supreme Court Oral Arguments
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Speech to Text to Semantics: A Sequence-to-Sequence System for Spoken Language Understanding
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Exploring Phone Recognition in Pre-verbal and Dysarthric Speech
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Abstract:
Thesis (Master's)--University of Washington, 2019 ; In this study, we perform phone recognition on speech utterances made by two groups of people: adults who have speech articulation disorders and young children learning to speak language. We explore how these utterances compare against those of adult English-speakers who don’t have speech disorders, training and testing several HMM-based phone-recognizers across various datasets. Experiments were carried out via the HTK Toolkit with the use of data from three publicly available datasets: the TIMIT corpus, the TalkBank CHILDES database and the Torgo corpus. Several discoveries were made towards identifying best-practices for phone recognition on the two subject groups, involving the use of optimized Vocal Tract Length Normalization (VTLN) configurations, phone-set reconfiguration criteria, specific configurations of extracted MFCC speech data and specific arrangements of HMM states and Gaussian mixture models.
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Keyword:
child speech; Computer science; dysarthria; Linguistics; machine learning; phone recognition; phonetics; speech-language pathology
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URL: http://hdl.handle.net/1773/44344
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Labeling and Automatically Identifying Basic-Level Categories
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Three Cheers For Partisanship: Lexical Framing and Applause in U.S. Presidential Primary Debates
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Detection of Agreement and Disagreement: An investigation of linguistic coordination and conversational features
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