arXiv:2605.26978v1 Announce Type: new Abstract: Text-to-speech (TTS) evaluation for low-resource non-Latin-script languages can fail when it relies on a single ASR round-trip word error rate (WER). A system may produce no audio, speak a neighbouring language, preserve target script text only in an ASR transcript, or sound unnatural to native listeners. We introduce INSV (Intelligibility, Naturalness, Script fidelity, and Verification), a reporting framework that separates these cases. This paper reports INSV-A, the automated screening subset: synthesis completion, ASR WER/CER, transcript Scrip
The proliferation of AI models demands robust evaluation methods for diverse linguistic contexts, especially for languages beyond common Western scripts.
This development addresses critical limitations in evaluating text-to-speech for low-resource, non-Latin-script languages, which can otherwise lead to flawed AI system deployment and perpetuate linguistic bias.
The introduction of the INSV framework and its automated screening subset (INSV-A) provides a more comprehensive and accurate method for assessing TTS quality, moving beyond simplistic single-metric evaluations.
- · AI developers focused on linguistic diversity
- · Speakers of low-resource languages
- · Speech technology researchers
- · Linguistic preservation efforts
- · Developers relying solely on ASR WER for evaluation
- · Legacy TTS evaluation methodologies
Improved evaluation leads to more effective and equitable text-to-speech systems for a wider range of global languages.
Enhanced quality and accessibility of TTS technology could accelerate digital inclusion for non-Latin script language communities.
The methodology could serve as a blueprint for evaluating other AI modalities in low-resource or complex linguistic contexts, reducing digital divides.
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Read at arXiv cs.CL