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

2025-07-19

AI Summary

Low automatic speech recognition (ASR) accuracy in industrial-scale CRM systems—stemming from poor generalization of off-the-shelf ASR models to domain-specific terminology and speaker accents—hampers downstream customer intent understanding. To address this, we propose a weakly supervised, domain-adaptive ASR fine-tuning framework tailored for CRM applications. Our method leverages noise-robust weak supervision to drastically reduce reliance on high-quality transcriptions; integrates lightweight acoustic model adaptation with domain-aware speech preprocessing and linguistic adaptation techniques; and enables efficient transfer of generic ASR models to vertical CRM scenarios. Evaluated on real-world CRM voice data, our approach achieves an average 32.7% relative reduction in word error rate (WER). Deployed in production CRM infrastructure, it robustly supports customer intent classification, entity typing, and personalized service generation—demonstrating strong practical deployability and cross-domain generalizability.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Speech
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Enhancing ASR models for industry-specific CRM systems
• Improving customer voice and intention recognition accuracy
• Fine-tuning ASR models for better industry application performance

Innovation

• Fine-tuning industry-specific ASR models
• Enhancing ASR performance in CRM
• Applying weak supervision techniques