Towards a Phonology-Informed Evaluation of Multilingual TTS
Neural TTS systems can sound natural across languages, but naturalness does not guarantee the preservation of sound contrasts that distinguish words from their grammatical forms. Standard metrics like MOS do not test for this. We propose a classifier-based framework that audits TTS output against language-specific phonological patterns using human speech as a benchmark. Testing Assamese advanced tongue root (ATR) vowel harmony with Meta's MMS TTS, we show that a classifier trained on human speech transfers to synthesized speech with minimal loss. The faithfulness audit reveals that [+ATR] mid vowels are realized as [-ATR] in 1/3 tokens despite an underlying [+ATR] specification, a bias ab...
| Authors | Sneha Ray Barman, Neeraj Kumar Sharma, Shakuntala Mahanta |
|---|---|
| Primary category | cs.CL |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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