arXiv Open Access 2025

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

Zhongsheng Wang Sijie Wang Jia Wang Yung-I Liang Yuxi Zhang +1 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.SD cs.AI cs.CL eess.AS

Penulis (6)

Zhongsheng Wang

Sijie Wang

Jia Wang

Yung-I Liang

Yuxi Zhang

Jiamou Liu

Format Sitasi

APA MLA BibTeX

Wang, Z., Wang, S., Wang, J., Liang, Y., Zhang, Y., Liu, J. (2025). Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems. https://arxiv.org/abs/2507.16843

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓