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

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Zhongsheng Wang , Shuishu Wang , Jia Wang , Yanling Liang , Yuxi Zhang , Jing Liu ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.16843

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2507.16843
PDF: https://arxiv.org/pdf/2507.16843
OA Status: green
OpenAlex ID: https://openalex.org/W4414877018

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DOI: https://doi.org/10.48550/arxiv.2507.16843

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

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-20

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Authors: Zhongsheng Wang, Shuishu Wang, Jia Wang, Yanling Liang, Yuxi Zhang, Jing Liu

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Landing page: https://arxiv.org/abs/2507.16843

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PDF URL: https://arxiv.org/pdf/2507.16843

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Cited by: 0

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