arXiv:2607.01965v1 Announce Type: new Abstract: 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 proliferation of advanced neural TTS systems necessitates more rigorous and nuanced evaluation methods beyond subjective naturalness to ensure speech quality and linguistic accuracy.
Sophisticated AI systems moving from 'sounding good' to 'linguistically accurate' has profound implications for global communication, education, and the reliability of AI-generated content across diverse languages.
The focus for TTS evaluation shifts from solely perceived naturalness to include critical tests of phonological integrity, pushing for more robust and culturally sensitive AI language capabilities.
- · Multilingual AI developers
- · Linguists and phoneticians
- · Education technology
- · Global content creators
- · Simply 'natural-sounding' but inaccurate TTS models
- · Developers neglecting low-resource language particularities
Improved TTS quality across diverse languages, particularly those with complex phonological rules.
Reduced linguistic bias and increased adoption of AI-generated speech in culturally sensitive applications, leading to better user acceptance.
Enhanced global accessibility and understanding, fostering more inclusive digital communication environments for previously underserved linguistic communities.
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