Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies
Recent alignment-free Text-to-Speech (TTS) models achieve high synthesis quality by removing explicit alignment modules and simplifying training. However, they often struggle in cross-lingual scenarios due to differences in character durations across languages, leading to duration mismatch and degraded speech quality. Existing approaches typically address this issue using explicit duration predictors, increasing model complexity. Systematic evaluation of TTS performance for Indian languages in cross-lingual settings remains under-explored due to the lack of standardized evaluation benchmarks. In this work, we construct a cross-lingual evaluation benchmark covering English and nine Indian ...
| Authors | Adarsh Arigala, Arjun Gangwar, Srinivasan Umesh |
|---|---|
| Primary category | eess.AS |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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