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New method enhances non-verbal vocalization synthesis in TTS systems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for synthesizing non-verbal vocalizations (NVs) in text-to-speech (TTS) systems, focusing on preference optimization techniques. They introduced an NV-aware character error rate (NV-CER) to control the realization of NVs like laughter and coughs without altering the core optimization algorithm. Experiments on the Emilia-NV dataset and the augmented NV-Bench demonstrated the effectiveness of their approach, providing practical guidance for improving expressive TTS. AI

IMPACT Enhances expressiveness in TTS systems, potentially leading to more natural and engaging synthetic voices.

RANK_REASON The cluster contains a research paper detailing a new method for synthesizing non-verbal vocalizations in TTS systems. [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 method enhances non-verbal vocalization synthesis in TTS systems

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The cluster contains a research paper detailing a new method for synthesizing non-verbal vocalizations in TTS systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Haoyang Li, Chenglin Xu, Junchuan Zhao, Yuang Cao, Liumeng Xue, Yiwen Guo, Eng Siong Chng ·

    Preference Optimization for Non-Verbal Vocalization Synthesis

    arXiv:2608.24163v1 Announce Type: cross Abstract: Non-verbal vocalizations (NVs), such as laughter, coughs, and sighs, are essential for expressive TTS, but the effectiveness of preference optimization for NV generation remains poorly understood. We systematically study preferenc…

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