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.