MultiGen用大模型生成适合儿童的多语言语音，支持低资源语种。
MultiGen: Child-Friendly Multilingual Speech Generator with LLMs
- 基于大模型架构实现适龄多语言语音生成
- 在三种低资源语言中表现优于基线方法
- 适合儿童语言学习与跨文化互动场景
生成式语音模型在提升人机交互方面展现出巨大潜力，尤其在儿童语言学习等实际应用中价值显著。然而，针对低资源语言在多样文化背景下的高质量、儿童友好型语音生成仍具挑战。本文提出MultiGen，一种基于大模型架构的多语言儿童友好语音生成模型，专为低资源语言设计。该模型通过适龄多语言语音生成，支持新加坡口音普通话、马来语和泰米尔语，让儿童能以符合文化背景的方式与AI交流。客观指标与主观评估结果均表明，MultiGen在性能上显著优于基线方法。
原文摘要 · Abstract (English)
Generative speech models have demonstrated significant potential in improving human-machine interactions, offering valuable real-world applications such as language learning for children. However, achieving high-quality, child-friendly speech generation remains challenging, particularly for low-resource languages across diverse languages and cultural contexts. In this paper, we propose MultiGen, a multilingual speech generation model with child-friendly interaction, leveraging LLM architecture for speech generation tailored for low-resource languages. We propose to integrate age-appropriate multilingual speech generation using LLM architectures, which can be used to facilitate young children's communication with AI systems through culturally relevant context in three low-resource languages: Singaporean accent Mandarin, Malay, and Tamil. Experimental results from both objective metrics and subjective evaluations demonstrate the superior performance of the proposed MultiGen compared to baseline methods.
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