Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems

TL;DR

This paper presents a novel weak supervision approach for fine-tuning industry-specific ASR models to improve speech recognition accuracy in CRM systems, demonstrating significant performance gains and practical industrial deployment.

Contribution

The paper introduces a weak supervision technique for customizing ASR models to industry-specific speech, enhancing recognition accuracy in CRM applications.

Findings

Significant improvement in ASR performance for industry-specific tasks.

Successful deployment of the method in real industrial CRM systems.

Enhanced customer voice recognition accuracy.

Abstract

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.