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.
Recently, a novel speaker adaptation method was proposed that applied the Speaker Adaptive Training (SAT) concept to a speech recognizer consisting of a Deep Neural Network (DNN) and a Hidden Markov Model (HMM), and its utility was demonstrated. This method implements the SAT scheme by allocating one Speaker Dependent (SD) module for each training speaker to one of the intermediate layers of the front-end...
Among many speaker adaptation embodiments, Speaker Adaptive Training (SAT) has been successfully applied to a standard Hidden-Markov-Model (HMM) speech recognizer, whose state is associated with Gaussian Mixture Models (GMMs). On the other hand, recent studies on Speaker-Independent (SI) recognizer development have reported that a new type of HMM speech recognizer, which replaces GMMs with Deep Neural...
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.