Abstract: There is provided servers and methods of generating a waveform based on a spectrogram and a noise input. The method includes acquiring a trained flow-based vocoder including invertible blocks, and an untrained feed-forward vocoder including non-invertible blocks, which form a student-teacher network. The method includes executing a training process in the student-teacher network during which the server generates (i) a teacher waveform by the trained flow-based vocoder using a first spectrogram and a first noise input, (ii) a student waveform by the untrained feed-forward vocoder using the first spectrogram and the first noise input, and (iii) a loss value for the given training iteration using the teacher waveform and the student waveform. The server then trains the untrained feed-forward vocoder to generate the waveform. The trained feed-forward vocoder in then used lieu of the trained flow-based vocoder for generating waveforms based on spectrograms and noise inputs.

Abstract: Methods and servers for processing a textual input for generating an audio output are disclosed. The audio output is a sequence of waveform segments generated in real-time by a trained Convolutional Neural Network. The method includes, at a given iteration, generating a given waveform segment which includes storing first tensor data computed by a first hidden layer during the given iteration, and where the first tensor data has tensor-chunk data. The tensor-chunk data is used during the given iteration for generating the given waveform segment and is to be used during a next iteration for generating a next waveform segment. The method includes, at the next iteration, generating the next waveform segment, which comprises storing second tensor data computed by the first hidden layer during the next iteration. The second tensor data excludes redundant tensor-chunk data that is identical to the tensor-chunk data from the first tensor data.

Abstract: There is disclosed a method of generating a text-to-speech (TTS) training set for training a Machine Learning Algorithm (MLA) for generating machine-spoken utterances The method is executable by a server. The method includes generating a synthetic word based on merging separate phonemes from each of two words of a corpus of pre-recorded utterances, the merging being done using the common phoneme as a merging anchor, the merging resulting in at least two synthetic words. The synthetic words and assessor labels are used to train a classifier to predict a quality parameter associated with a new synthetic phonemes-based word, the quality parameter being representative of whether the new synthetic phonemes-based word is naturally sounding (based on acoustic features of generated synthetic words utterances). The classifier is then used to generate training objects for the MLA and to use the MLA to process the corpus of pre-recorded utterances into their respective vectors.

Abstract: There is disclosed a method of generating a text-to-speech (TTS) training set for training a Machine Learning Algorithm (MLA) for generating machine-spoken utterances The method is executable by a server. The method includes generating a synthetic word based on merging separate phonemes from each of two words of a corpus of pre-recorded utterances, the merging being done using the common phoneme as a merging anchor, the merging resulting in at least two synthetic words. The synthetic words and assessor labels are used to train a classifier to predict a quality parameter associated with a new synthetic phonemes-based word, the quality parameter being representative of whether the new synthetic phonemes-based word is naturally sounding (based on acoustic features of generated synthetic words utterances). The classifier is then used to generate training objects for the MLA and to use the MLA to process the corpus of pre-recorded utterances into their respective vectors.