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.
Building synthetic child voices is considered a difficult task due to the challenges associated with data collection. As a result, speaker adaptation in conjunction with Hidden Markov Model (HMM)-based synthesis has become prevalent in this domain because the approach caters for limited amounts of data. An initial average voice model is trained using data from multiple speakers and adapted to resemble...
Deep Learning has been applied successfully to speech processing. In this paper we propose an architecture for speech synthesis using multiple speakers. Some hidden layers are shared by all the speakers, while there is a specific output layer for each speaker. Objective and perceptual experiments prove that this scheme produces much better results in comparison with single speaker model. Moreover,...
The statistical models of hidden Markov model based text-to-speech (HMM-TTS) systems are typically built using homogeneous data. It is possible to acquire data from many different sources but combining them leads to a non-homogeneous or diverse dataset. This paper describes the application of average voice models (AVMs) and a novel application of cluster adaptive training (CAT) with multiple context...
Reducing the recording effort required in practical speaker adaptive text-to-speech applications would be very useful. In this paper, we present two sentence selection approaches based on a greedy algorithm; one is based on phone coverage and the other is based on model coverage. The former considers the phonetic information in speaker adaptation data, while the latter focuses on occurrences of Mel-cepstral...
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.