Visualising Model Training via Vowel Space for Text-To-Speech Systems

TL;DR

This paper demonstrates how visualising vowel space changes during training can effectively evaluate and understand speech synthesis models' learning process, combining linguistic insights with model analysis.

Contribution

It introduces a method to visualise and evaluate speech synthesis training progress through vowel space analysis, linking linguistic features with model learning.

Findings

Vowel space visualization reflects model learning stages.

Perception tests confirm humans perceive learned speech characteristics.

Vowel space analysis aids in improving speech synthesis quality.

Abstract

With the recent developments in speech synthesis via machine learning, this study explores incorporating linguistics knowledge to visualise and evaluate synthetic speech model training. If changes to the first and second formant (in turn, the vowel space) can be seen and heard in synthetic speech, this knowledge can inform speech synthesis technology developers. A speech synthesis model trained on a large General American English database was fine-tuned into a New Zealand English voice to identify if the changes in the vowel space of synthetic speech could be seen and heard. The vowel spaces at different intervals during the fine-tuning were analysed to determine if the model learned the New Zealand English vowel space. Our findings based on vowel space analysis show that we can visualise how a speech synthesis model learns the vowel space of the database it is trained on. Perception tests confirmed that humans could perceive when a speech synthesis model has learned characteristics of the speech database it is training on. Using the vowel space as an intermediary evaluation helps understand what sounds are to be added to the training database and build speech synthesis models based on linguistics knowledge.