根据音频自动选最优语音识别模型，提升质量降成本
AutoMode-ASR: Learning to Select ASR Systems for Better Quality and Cost
- 用二分类器集成判断不同模型优劣，按音频选最佳系统
- WER降16.2%，成本降65%，速度提75%
- 兼容商业与开源模型，无需修改代码
我们提出AutoMode-ASR，一种新框架，通过有效整合多个语音识别系统，在提升整体转录质量的同时优化成本。核心思路是训练一个决策模型，仅根据音频输入，提前为每个语音段选择最优的ASR系统。方法基于集成二分类器，用于判断两个系统之间的偏好关系，这些分类器利用音频嵌入、质量估计和信号特性等多种特征。此外，我们证明了使用质量估计器可进一步提升性能，且成本增加极小。实验结果表明，相比对所有段落使用单一最优模型，该框架实现相对词错误率（WER）降低16.2%，成本节省65%，推理速度提升75%。该框架兼容商用及开源黑盒ASR系统，无需修改模型代码。
原文摘要 · Abstract (English)
We present AutoMode-ASR, a novel framework that effectively integrates multiple ASR systems to enhance the overall transcription quality while optimizing cost. The idea is to train a decision model to select the optimal ASR system for each segment based solely on the audio input before running the systems. We achieve this by ensembling binary classifiers determining the preference between two systems. These classifiers are equipped with various features, such as audio embeddings, quality estimation, and signal properties. Additionally, we demonstrate how using a quality estimator can further improve performance with minimal cost increase. Experimental results show a relative reduction in WER of 16.2%, a cost saving of 65%, and a speed improvement of 75%, compared to using a single-best model for all segments. Our framework is compatible with commercial and open-source black-box ASR systems as it does not require changes in model codes.
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