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Integrate template matching and statistical modeling for continuous speech recognition
(University of Missouri--Columbia, 2011)
In this dissertation, a novel approach of integrating template matching with statistical modeling is proposed to improve continuous speech recognition. Commonly used Hidden Markov Models (HMMs) are ineffective in modeling details of speech temporal...
Statistical optimization of acoustic models for large vocabulary speech recognition
(University of Missouri--Columbia, 2006)
This dissertation investigates optimization of acoustic models in speech recognition. Two new optimization methods are proposed for phonetic decision tree (PDT) search and Hidden Markov modeling (HMM)-- the knowledge-based ...
Language modeling for automatic speech recognition in telehealth
(University of Missouri--Columbia, 2005)
Standard statistic n-gram language models play a critical and indispensable role in automatic speech recognition (ASR) applications. Though helpful to ASR, it suffers from a practical problem when lacking sufficient in-domain training data that come...
Ensemble methods in large vocabulary continuous speech recognition
(University of Missouri--Columbia, 2008)
Combining a group of classifiers and therefore improving the overall classification performance is a young and promising direction in Large Vocabulary Continuous Speech Recognition (LVCSR). Previous works on acoustic ...
Selecting data for multilingual multi-domain neural machine translation on low resource languages
(University of Missouri--Columbia, 2020)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] While machine translation has achieved impressive results on the world's most widely spoken languages, thousands of languages do not have the quantity ...
Enhancement of adaptive de-correlation filtering separation model for robust speech recognition
(University of Missouri--Columbia, 2007)
The development of automatic speech recognition (ASR) technology has enabled an increasing number of applications. However, the robustness of ASR under real acoustic environments still remains to be a challenge for practical ...
Improvement of decoding engine & phonetic decision tree in acoustic modeling for online large vocabulary conversational speech recognition
(University of Missouri--Columbia, 2007)
In this work, new approaches are proposed for online large vocabulary conversational speech recognition, including a fast confusion network algorithm, novel features and a Random Forests based classifier for word confidence ...
Modeling of the acoustic signal of an electric guitar amplifier using recurrent neural networks
(University of Missouri--Columbia, 2021)
Neural networks have topped performance measures across a wide variety of computational tasks. These performances are prevalent within the domain of human perception type tasks such as classification or generation of images, ...