用弱监督微调行业ASR模型，提升客户语音识别准确率。
Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems
- 通过弱监督技术微调行业专用ASR模型
- 显著提升工业级CRM系统中语音识别性能
- 已应用于实际工业场景，适合语音分析需求方
在客户关系管理（CRM）系统设计中，精准识别客户类型并提供个性化服务是提升客户满意度和忠诚度的关键。然而，该过程面临辨别客户语音与意图的挑战，通用预训练自动语音识别（ASR）模型难以有效应对行业特定语音识别任务。为此，我们创新性地提出一种针对行业专用ASR模型的微调方法，显著提升了细调后ASR模型在工业应用中的表现。实验结果表明，该方法大幅增强了ASR模型在工业级CRM系统中的关键辅助作用，该方案已在实际工业应用中落地。
原文摘要 · Abstract (English)
In the design of customer relationship management (CRM) systems, accurately identifying customer types and offering personalized services are key to enhancing customer satisfaction and loyalty. However, this process faces the challenge of discerning customer voices and intentions, and general pre-trained automatic speech recognition (ASR) models make it difficult to effectively address industry-specific speech recognition tasks. To address this issue, we innovatively proposed a solution for fine-tuning industry-specific ASR models, which significantly improved the performance of the fine-tuned ASR models in industry applications. Experimental results show that our method substantially improves the crucial auxiliary role of the ASR model in industry CRM systems, and this approach has also been adopted in actual industrial applications.
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