1. A method for text-to-speech synthesis configured to output a synthetic speech having two or more selected speech attributes, the method executable at a computing device, the method comprising the steps of: a) receiving a training text data and a respective training acoustic data, the respective training acoustic data being a spoken representation of the training text data, the respective training acoustic data being associated with one or more defined speech attribute; b) extracting one or more of phonetic and linguistic features of the training text data; c) extracting vocoder features of the respective training acoustic data, and correlating the vocoder features with the phonetic and linguistic features of the training text data and with the one or more defined speech attribute, thereby generating a set of training data of speech attributes; d) using a deep neural network (dnn) 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; e) receiving a text; f) receiving a selection of the two or more speech attributes, the two or more selected speech attributes having respective selected attribute weights desired in a synthetic speech to be outputted; g) converting the text into the synthetic speech using said acoustic space model, the synthetic speech having a weighted sum of the two or more selected speech attributes; and h) outputting the synthetic speech as audio having the two or more speech attributes having the respective selected attribute weights desired in the synthetic speech.
1. A method for text-to-speech synthesis configured to output a synthetic speech having two or more selected speech attributes, the method executable at a computing device, the method comprising the steps of: a) receiving a training text data and a respective training acoustic data, the respective training acoustic data being a spoken representation of the training text data, the respective training acoustic data being associated with one or more defined speech attribute; b) extracting one or more of phonetic and linguistic features of the training text data; c) extracting vocoder features of the respective training acoustic data, and correlating the vocoder features with the phonetic and linguistic features of the training text data and with the one or more defined speech attribute, thereby generating a set of training data of speech attributes; d) using a deep neural network (dnn) 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; e) receiving a text; f) receiving a selection of the two or more speech attributes, the two or more selected speech attributes having respective selected attribute weights desired in a synthetic speech to be outputted; g) converting the text into the synthetic speech using said acoustic space model, the synthetic speech having a weighted sum of the two or more selected speech attributes; and h) outputting the synthetic speech as audio having the two or more speech attributes having the respective selected attribute weights desired in the synthetic speech. 2. The method of claim 1 , wherein said extracting one or more of phonetic and linguistic features of the training text data comprises dividing the training text data into phones.