Enhancing Multilingual Speech Generation and Recognition Abilities in LLMs with Constructed Code-switched Data

Jing Xu, Daxin Tan, Jiaqi Wang, Xiao Chen
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Abstract

While large language models (LLMs) have been explored in the speech domain for both generation and recognition tasks, their applications are predominantly confined to the monolingual scenario, with limited exploration in multilingual and code-switched (CS) contexts. Additionally, speech generation and recognition tasks are often handled separately, such as VALL-E and Qwen-Audio. In this paper, we propose a MutltiLingual MultiTask (MLMT) model, integrating multilingual speech generation and recognition tasks within the single LLM. Furthermore, we develop an effective data construction approach that splits and concatenates words from different languages to equip LLMs with CS synthesis ability without relying on CS data. The experimental results demonstrate that our model outperforms other baselines with a comparable data scale. Furthermore, our data construction approach not only equips LLMs with CS speech synthesis capability with comparable speaker consistency and similarity to any given speaker, but also improves the performance of LLMs in multilingual speech generation and recognition tasks.
利用建构的代码切换数据增强 LLM 中的多语言语音生成和识别能力
虽然大语言模型(LLM)在语音领域的生成和识别任务中都有所探索,但其应用主要局限于单语言场景,在多语言和代码转换(CS)语境中的探索有限。此外,语音生成和识别任务通常是分开处理的,如 VALL-E 和 Qwen-Audio。在本文中,我们提出了一种多语言多任务(MutltiLingual MultiTask,MLMT)模型,将多语言语音生成和识别任务整合到单个 LLM 中。实验结果表明,在数据规模相当的情况下,我们的模型优于其他基线模型。此外,我们的数据构建方法不仅使 LLM 具备了 CS 语音合成能力,而且说话人的一致性和相似性与任何给定说话人相当,同时还提高了 LLM 在多语言语音生成和识别任务中的性能。
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