Abstract: Artificial Intelligence-based (AI-based) modeling can be used to predict “Critical Times” when “bottlenecks” in a processing of data would occur. Moreover, for each one of the predicted Critical Times, it can be determined which one of multiple Computing Resources would cause the bottleneck, so that more precise measures can be taken and taken before a Critical Time, in an effort to prevent bottlenecks from happening in computing systems, especially more complex database systems with more demeaning service needs and requirements.

Abstract: Artificial Intelligence-based (AI-based) modeling can be used to predict “Critical Times” when “bottlenecks” in a processing of data would occur. Moreover, for each one of the predicted Critical Times, it can be determined which one of multiple Computing Resources would cause the bottleneck, so that more precise measures can be taken and taken before a Critical Time, in an effort to prevent bottlenecks from happening in computing systems, especially more complex database systems with more demeaning service needs and requirements.

Abstract: A method of modifying a speech signal generated by a text-to-speech synthesiser, the method comprising: receiving a text signal; generating a speech signal from the text signal; deriving a control feature vector, wherein the control feature vector represents modifications to the speech signal; inputting the control feature vector in the text-to-speech synthesiser, wherein the text-to-speech synthesiser is configured to generate a modified speech signal using the control feature vector; and outputting the modified speech signal.

Abstract: A method for synthesising speech from text includes receiving text and encoding, by way of an encoder module, the received text. The method further includes determining, by way of an attention module, a context vector from the encoding of the received text, wherein determining the context vector comprises at least one of: applying a threshold function to an attention vector and accumulating the thresholded attention vector, or applying an activation function to the attention vector and accumulating the activated attention vector. The method further includes determining speech data from the context vector.