The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
A new parameter estimation method for the model-Based feature enhancement (MBFE) is presented. The conventional MBFE uses the vector Taylor series to calculate the parameters of non-linearly transformed distributions, though the linearization leads to a degraded performance. We use the unscented transformation to estimate the parameters, where a minimal number of samples propagated through the nonlinear...
The SPLICE method of feature enhancement is known for its powerful performance. It learns a mapping from noisy to clean feature vectors given a set of stereo training data. However, feature vector variation caused by speaker changes conceals noise-induced variation, which is what we want to find in the SPLICE training. In this paper, an improvement of SPLICE by means of speaker-normalization is proposed...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.