利用从未发生的对话进行高效的ASR训练
Efficient ASR Training with Conversations that Never Happened
AI总结：
针对低资源语言和特定领域，提出通过LLM生成对话场景、映射说话人属性到TTS语音配置文件并组装合成话语的增强流水线，实验表明合成对话能有效提升ASR性能，在匈牙利语基准上仅用67小时真实对话和636小时模拟数据即超越2700小时零样本模型。
摘要：
低资源语言和特定领域的对话式ASR受到领域匹配的多说话人训练数据稀缺的限制。我们提出了一种增强流水线，该流水线生成带有参与者元数据的场景级对话，将说话人属性映射到TTS语音配置文件，并将合成的话语组装成感知说话人的模拟对话。我们在相同的FastConformer-Large训练方案下，评估了五种LLM家族，分别采用单生成器、固定预算混合和扩展设置。我们在匈牙利语BEA-Dialogue基准语料库上进行了全面评估，该方法本身适用于任何语言，只要各组件有相应资源。结果表明，合成对话持续改善语音识别性能，但生成器选择和组成数据强烈影响增益。我们最大的训练配置仅使用67小时真实对话和636小时模拟数据，在评估基准上实现了比在2700小时匈牙利语语音上训练的零样本模型更好的性能。这些发现表明，通过TTS合成的LLM生成的对话数据是真实对话语料库在语音模型训练中的实用补充。
英文摘要：
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations. We evaluated five LLM families under single-generator, fixed-budget mixture, and scale-up settings using the same FastConformer-Large training recipe for each one. We ran comprehensive evaluations on the Hungarian BEA-Dialogue benchmark corpus, with the method itself being applicable to any language given the resources for each component. The results show that synthetic conversations consistently improve speech recognition performance, but generator choice and data composition strongly affect the gains. Our largest training configuration, using only 67 hours of real conversations and 636 hours of simulated data, achieves better performance on the evaluation benchmark than a zero-shot model trained on 2700 hours of Hungarian speech. These findings indicate that LLM-generated conversational data synthesized with TTS is a practical complement to real conversational corpora for speech model training.
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