Global ETD Search

1	Extraction of Basic Noun Phrases from Natural Language Using Statistical Context-Free Grammar Afrin, Taniza 31 May 2001 (has links) The objective of this research was to extract simple noun phrases from natural language texts using two different grammars: stochastic context-free grammar (SCFG) and non-statistical context free grammar (CFG). Precision and recall were calculated to determine how many precise and correct noun phrases were extracted using these two grammars. Several text files containing sentences from English natural language specifications were analyzed manually to obtain the test-set of simple noun-phrases. To obtain precision and recall, this test-set of manually extracted noun phrases was compared with the extracted-sets of noun phrases obtained using the both grammars SCFG and CFG. A probabilistic chart parser was developed by modifying a deterministic parallel chart parser. Extraction of simple noun-phrases with the SCFG was accomplished using this probabilistic chart parser, a dictionary containing word probabilities along with the meaning, context-free grammar rules associated with rule probabilities and finally an algorithm to extract most likely parses of a sentence. The probabilistic parsing algorithm and the algorithm to determine figures of merit were implemented using C++ programming language. / Master of Science Information Extraction Probabilistic Parser Noun Phrase Stochastic Grammar
2	Machine Learning for Speech Forensics and Hypersonic Vehicle Applications Emily R Bartusiak (6630773) 06 December 2022 (has links) <p>Synthesized speech may be used for nefarious purposes, such as fraud, spoofing, and misinformation campaigns. We present several speech forensics methods based on deep learning to protect against such attacks. First, we use a convolutional neural network (CNN) and transformers to detect synthesized speech. Then, we investigate closed set and open set speech synthesizer attribution. We use a transformer to attribute a speech signal to its source (i.e., to identify the speech synthesizer that created it). Additionally, we show that our approach separates different known and unknown speech synthesizers in its latent space, even though it has not seen any of the unknown speech synthesizers during training. Next, we explore machine learning for an objective in the aerospace domain.</p> <p><br></p> <p>Compared to conventional ballistic vehicles and cruise vehicles, hypersonic glide vehicles (HGVs) exhibit unprecedented abilities. They travel faster than Mach 5 and maneuver to evade defense systems and hinder prediction of their final destinations. We investigate machine learning for identifying different HGVs and a conic reentry vehicle (CRV) based on their aerodynamic state estimates. We also propose a HGV flight phase prediction method. Inspired by natural language processing (NLP), we model flight phases as “words” and HGV trajectories as “sentences.” Next, we learn a “grammar” from the HGV trajectories that describes their flight phase transition patterns. Given “words” from the initial part of a HGV trajectory and the “grammar”, we predict future “words” in the “sentence” (i.e., future HGV flight phases in the trajectory). We demonstrate that this approach successfully predicts future flight phases for HGV trajectories, especially in scenarios with limited training data. We also show that it can be used in a transfer learning scenario to predict flight phases of HGV trajectories that exhibit new maneuvers and behaviors never seen before during training.</p> Audio processing Computer vision Digital forensics Deep learning machine learning deep learning speech forensics media forensics convolutional neural network transformer convolutional transformer ensemble spectrogram analysis mel spectrogram analysis synthesized speech detection synthesized speech attribution closed set open set t-stochastic neighbor embedding latent space analysis hypersonics hypersonic glide vehicles vehicle classification flight phase prediction stochastic grammar k-nearest neighbors support vector machine probabilistic context-free grammar automatic distillation of structure generalized earley parser transfer learning

Search results

Extraction of Basic Noun Phrases from Natural Language Using Statistical Context-Free Grammar

Machine Learning for Speech Forensics and Hypersonic Vehicle Applications