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

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.

An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space

“…We extend the previous vowel space analysis approach [12] from mono-lingual to cross-lingual scenarios. Our vowel analysis method comprises the following steps.…”

“…As in [12], the F 1 and F 2 values are extracted at the middle point of the chosen segment. We synthesize 100 samples per vowel, and set the median as the representative.…”

“…Our approach has some advantages over the previous method [12]. First, it requires no external aligner or segmentation, thus it is free from any potential errors caused by an external segmentation tool.…”

“…Third, it is closer to the actual usage scenario of TTS systems than the previous method. In the previous approach [12], multiple hVd words with an in-between pause are given as text input. However, this is slightly astray from what a TTS system usually is trained for.…”

“…Abeysinghe, et al [12] propose to utilize the vowel space analysis as an intermediary evaluation tool to assess accents or dialects of English. In this paper, we extend their vowel space analysis approach [12] to cross-lingual TTS systems. We propose a cross-lingual vowel space analysis method and probe L2 accents in cross-lingual TTS systems.…”