Computing system for unsupervised emotional text to speech training

A method for training a text to speech model (TTS) that, the method comprising:

• obtaining training data for training the TTS model, wherein the training data comprises text samples and audio samples, wherein the training data does not include emotional labels indicative of one or more emotions expressed in the text samples and the audio samples;
• providing the text samples to a text embedding model, wherein the text embedding model outputs text embeddings for the text samples wherein the text embeddings comprise one or more emotions encoded in the text embeddings by the text embedding model;
• clustering the text embeddings into several clusters of text embeddings, wherein the several clusters are representative of variations in emotion encoded in the textual embeddings;
• and
• training the TTS model based upon the several clusters of text embeddings, wherein the TTS model, upon being trained, is configured to receive text input and output a spoken utterance that corresponds to the text input, wherein the TTS model is configured to output the spoken utterance with an emotion, and further wherein the emotion is based upon the text input and the training of the text to speech model.

A non-transitory computer-readable storage medium comprising instructions that, when executed by a processor of a computing device, cause the processor to perform acts comprising:

• receiving training data for training a text to speech (TTS) model, wherein the training data includes text that comprises text samples, wherein the training data does not include emotional labels indicative of one or more emotions expressed in the text;
• and
• training the TTS model based upon the training data, wherein training the TTS model comprises:
• providing the text samples to a text embedding model, wherein the text embedding model outputs text embeddings for the text samples, wherein the text embeddings comprise one or more emotions encoded in the text embeddings by the text embedding model;
• clustering the text embeddings into several clusters of text embeddings, wherein the several clusters are representative of variations in emotion encoded in the textual embeddings;
• and
• training the TTS model based upon the several clusters of text embeddings, wherein the TTS model, upon being trained, is configured to receive text input and output a spoken utterance that corresponds to the text input, wherein the TTS model is configured to output the spoken utterance with an emotion, and further wherein the emotion is based upon the text input and the training of the text to speech model.