面向说话人的模拟提升对话语音识别
Speaker-Aware Simulation Improves Conversational Speech Recognition
AI总结：
本文提出面向说话人的模拟对话方法，通过生成合成对话数据提升匈牙利语对话语音识别性能。
摘要：
自动语音识别（ASR）在对话语音领域仍面临挑战，原因在于大规模、高质量多说话人对话数据的获取有限，以及自然交互的复杂时间动态。面向说话人的模拟对话（SASC）通过将单说话人录音转换为真实多说话人对话，提供了一种有效的数据增强策略。然而，先前的研究主要集中在英语数据上，留下关于低资源语言适用性的问题。在本文中，我们适应并实现了SASC框架用于匈牙利语对话ASR。我们进一步提出了C-SASC，一种扩展变体，通过在话语持续时间上建模停顿，使更忠实表示人类对话中观察到的局部时间依赖性，同时保留原始方法的简洁和效率。我们从BEA-Large语料库生成合成匈牙利语对话，并将其与真实对话数据结合用于ASR训练。SASC和C-SASC在多种模拟配置下进行了广泛评估，使用来自CallHome、BEA-Dialogue和GRASS语料库的对话统计信息。实验结果表明，面向说话人的对话模拟在基于简单拼接的增强上持续提升识别性能。虽然C-SASC中的额外持续时间建模带来了一定但系统性的增益——特别是在字符级错误率上——但其有效性取决于源对话统计信息与目标领域的一致性。总体而言，我们的发现证实了面向说话人的对话模拟在匈牙利语ASR中的稳健性，并突显了在合成对话生成中日益详细的时间建模的优缺点。
英文摘要：
Automatic speech recognition (ASR) for conversational speech remains challenging due to the limited availability of large-scale, well-annotated multi-speaker dialogue data and the complex temporal dynamics of natural interactions. Speaker-aware simulated conversations (SASC) offer an effective data augmentation strategy by transforming single-speaker recordings into realistic multi-speaker dialogues. However, prior work has primarily focused on English data, leaving questions about the applicability to lower-resource languages. In this paper, we adapt and implement the SASC framework for Hungarian conversational ASR. We further propose C-SASC, an extended variant that incorporates pause modeling conditioned on utterance duration, enabling a more faithful representation of local temporal dependencies observed in human conversation while retaining the simplicity and efficiency of the original approach. We generate synthetic Hungarian dialogues from the BEA-Large corpus and combine them with real conversational data for ASR training. Both SASC and C-SASC are evaluated extensively under a wide range of simulation configurations, using conversational statistics derived from CallHome, BEA-Dialogue, and GRASS corpora. Experimental results show that speaker-aware conversational simulation consistently improves recognition performance over naive concatenation-based augmentation. While the additional duration conditioning in C-SASC yields modest but systematic gains--most notably in character-level error rates--its effectiveness depends on the match between source conversational statistics and the target domain. Overall, our findings confirm the robustness of speaker-aware conversational simulation for Hungarian ASR and highlight the benefits and limitations of increasingly detailed temporal modeling in synthetic dialogue generation.
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