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.
This paper investigates a new voice conversion technique using phone-aware Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs). Most existing voice conversion methods, including Joint Density Gaussian Mixture Models (JDGMMs), Deep Neural Networks (DNNs) and Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNNs), only take acoustic information of speech as features to...
Model based VAD approaches have been widely used and achieved success in practice. These approaches usually cast VAD as a frame-level classification problem and employ statistical classifiers, such as Gaussian Mixture Model (GMM) or Deep Neural Network (DNN) to assign a speech/silence label for each frame. Due to the frame independent assumption classification, the VAD results tend to be fragile....
Voice activity detection (VAD) is an important step for real-world automatic speech recognition (ASR) systems. Deep learning approaches, such as DNN, RNN or CNN, have been widely used in model-based VAD. Although they have achieved success in practice, they are developed on different VAD tasks separately. Whilst VAD performance under noisy conditions, especially with unseen noise or very low SNR,...
Voice activity detection (VAD) plays a crucial role in speech processing, especially in automatic speech recognition (ASR). It identifies the boundaries of the speech to be recognized and the boundary accuracies may significantly affect the recognition performance. Conventional VAD evaluation criteria are mostly based on frame-level accuracy of speech/non-speech classification, which may result in...
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.