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.
In this paper, features based on the sparse representation (SR) are proposed for the classification of speech units. The proposed method employs multiple dictionaries to effectively model variations present in the speech signal. Here, a Gaussian mixture model (GMM) is built using spectral features corresponding to frames of all the examples of a speech class. Multiple dictionaries corresponding to...
In this paper, we have employed learned dictionaries to compute sparse representation of speech utterances, which will be used to reduce the footprint of unit selection based speech synthesis (USS) systems. Speech database labeled at phoneme level is used to obtain multiple examples of the same phoneme, and all the examples (of each phoneme) are then used to learn a single overcomplete dictionary...
This paper proposes an approach based on compressed sensing to reduce the footprint of speech corpus in unit selection based speech synthesis (USS) systems. It exploits the observation that speech signal can have a sparse representation (in suitable choice of basis functions) and can be estimated effectively using the sparse coding framework. Thus, only few significant coefficients of the sparse vector...
Supervised approaches for speech enhancement require models to be learned for different noisy environments, which is a difficult criterion to meet in practical scenarios. In this paper, compressed sensing (CS) based supervised speech enhancement approach is proposed, where model (dictionary) for noise is derived from the noisy speech signal. It exploits the observation that unvoiced/silence regions...
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.