Abstract: There are disclosed methods and systems for text-to-speech synthesis for outputting a synthetic speech having a selected speech attribute. First, an acoustic space model is trained based on a set of training data of speech attributes, using a deep neural network to determine interdependency factors between the speech attributes in the training data, the dnn generating a single, continuous acoustic space model based on the interdependency factors, the acoustic space model thereby taking into account a plurality of interdependent speech attributes and allowing for modelling of a continuous spectrum of the interdependent speech attributes. Next, a text is received; a selection of one or more speech attribute is received, each speech attribute having a selected attribute weight; the text is converted into synthetic speech using the acoustic space model, the synthetic speech having the selected speech attribute; and the synthetic speech is outputted as audio having the selected speech attribute.

Abstract: There are disclosed methods and systems for text-to-speech synthesis for outputting a synthetic speech having a selected speech attribute. First, an acoustic space model is trained based on a set of training data of speech attributes, using a deep neural network to determine interdependency factors between the speech attributes in the training data, the dnn generating a single, continuous acoustic space model based on the interdependency factors, the acoustic space model thereby taking into account a plurality of interdependent speech attributes and allowing for modelling of a continuous spectrum of the interdependent speech attributes. Next, a text is received; a selection of one or more speech attribute is received, each speech attribute having a selected attribute weight; the text is converted into synthetic speech using the acoustic space model, the synthetic speech having the selected speech attribute; and the synthetic speech is outputted as audio having the selected speech attribute.