aiqbench analysis
This research introduces a training-free framework that resolves accent leakage in code-switching text-to-speech by applying localized language guidance to specific phrases. By utilizing self-attention probing to identify phrase boundaries, the method ensures native pronunciation for inserted foreign segments while preserving the primary speaker's identity. The approach advances speech synthesis capabilities without requiring fine-tuning or auxiliary models, marking a significant step toward more natural cross-lingual audio generation.
Phrase-Localized Language-Contrastive Guidance: Training-Free Localized Accent Control for Code-Switching Text-to-Speech
arXiv:2609.01016v1 Announce Type: new Abstract: Current speech synthesis struggles with code-switching, which mixes a foreign language phrase into a primary language utterance, causing the phrase to be spoken with the primary language's accent rather than its native one. We propose Phrase-Localized Language-Contrastive Guidance (LCG), a training-free inference framework that restores a native accent to code-switched phrases in cross-lingual text-to-speech. LCG replaces the single language guidance applied across the whole utterance with a separate guidance for each region, so each part is guided by its own language. To choose where to apply this localized guidance, we propose a self-attention probing technique that finds the phrase boundaries without external alignments. Together, these components generate speech in which each region carries the accent of its own language, requiring no fine-tuning or auxiliary models. Across diverse language pairs, LCG robustly increases the nativeness of the code-switched phrase while suppressing accent leakage, and preserving overall speaker identity and naturalness.
Abstract:Current speech synthesis struggles with code-switching, which mixes a foreign language phrase into a primary language utterance, causing the phrase to be spoken with the primary language's accent rather than its native one. We propose Phrase-Localized Language-Contrastive Guidance (LCG), a training-free inference framework that restores a native accent to code-switched phrases in cross-lingual text-to-speech. LCG replaces the single language guidance applied across the whole utterance with a separate guidance for each region, so each part is guided by its own language. To choose where to apply this localized guidance, we propose a self-attention probing technique that finds the phrase boundaries without external alignments. Together, these components generate speech in which each region carries the accent of its own language, requiring no fine-tuning or auxiliary models. Across diverse language pairs, LCG robustly increases the nativeness of the code-switched phrase while suppressing accent leakage, and preserving overall speaker identity and naturalness.
Accepted to EMNLP 2026 (Main Conference). Demo: this https URL
Computation and Language (cs.CL); Sound (cs.SD)
arXiv:2609.01016 [cs.CL]
(or arXiv:2609.01016v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.01016 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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