Pre-processing of Speech Signals for Robust Parameter Estimation (2023)
Abstract / truncated to 115 words
The topic of this thesis is methods of pre-processing speech signals for robust estimation of model parameters in models of these signals. Here, there is a special focus on the situation where the desired signal is contaminated by colored noise. In order to estimate the speech signal, or its voiced and unvoiced components, from a noisy observation, it is important to have robust estimators that can handle colored and non-stationary noise. Two important aspects are investigated. The first one is a robust estimation of the speech signal parameters, such as the fundamental frequency, which is required in many contexts. For this purpose, fast estimation methods based on a simple white Gaussian noise (WGN) assumption are ... toggle 12 keywordspre-whitening – colored – autoregressive – NMF – pitch – fundamental frequency – decomposition – voiced – unvoiced – optimal filtering – hybrid speech model – noise psd
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