MultiGen: Child-Friendly Multilingual Speech Generator with LLMs
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914021484003328 |
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| author | Gao, Xiaoxue Zhang, Huayun Chen, Nancy F. |
| author_facet | Gao, Xiaoxue Zhang, Huayun Chen, Nancy F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08715 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MultiGen: Child-Friendly Multilingual Speech Generator with LLMs Gao, Xiaoxue Zhang, Huayun Chen, Nancy F. Audio and Speech Processing Artificial Intelligence Computation and Language Signal Processing 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. |
| title | MultiGen: Child-Friendly Multilingual Speech Generator with LLMs |
| topic | Audio and Speech Processing Artificial Intelligence Computation and Language Signal Processing |
| url | https://arxiv.org/abs/2508.08715 |