Search
Now showing items 1-5 of 5
Towards deep learning on speech recognition for Khmer language
(University of Missouri--Columbia, 2016)
In order to perform speech recognition well, a huge amount of transcribed speech and textual data in the target language must be available for system training. The high demand for language resources constrains the development ...
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 ...
Ensemble acoustic modeling in automatic speech recognition
(University of Missouri--Columbia, 2011)
In this dissertation, several new approaches of using data sampling to construct an Ensemble of Acoustic Models (EAM) for speech recognition are proposed. A straightforward method of data sampling is Cross Validation (CV) ...
An exploration of composite language modeling for speech recognition
(University of Missouri--Columbia, 2013)
Language models are one of the most critical knowledge sources of automatic speech recognition (ASR) systems. In the past decades, many language models have been developed, and some have proved useful and successful in ...
Modulation domain processing and speech phase spectrum in speech enhancement
(University of Missouri--Columbia, 2012)
Clean speech signal is often accompanied by various kinds of interferences, such as background noise, reverberation, and competing speech. These interferences degrade speech perceptual quality and intelligibility, and ...