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New benchmark and strategies improve cross-lingual TTS for Indian languages

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new benchmark and strategies for cross-lingual Text-to-Speech (TTS) systems, specifically focusing on Indian languages. The work addresses challenges in alignment-free TTS models that struggle with duration mismatches across languages, leading to degraded speech quality. By creating a benchmark covering English and nine Indian languages, and proposing an inference-time duration estimation strategy that uses language-specific statistics without retraining, the team has improved speech naturalness, intelligibility, and speaker similarity. AI

IMPACT Improves cross-lingual TTS capabilities, particularly for under-resourced Indian languages, potentially enabling more natural and accessible voice technologies.

RANK_REASON Academic paper detailing a new benchmark and methodology for TTS. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark and strategies improve cross-lingual TTS for Indian languages

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Academic paper detailing a new benchmark and methodology for TTS. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Adarsh Arigala, Arjun Gangwar, Srinivasan Umesh ·

    Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies

    arXiv:2610.14826v1 Announce Type: cross Abstract: 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 charac…

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