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

arXiv 2507.16843 ↗First posted 20 Jul 2025cs.SD cs.AI cs.CLcited by 0

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Abstract (arXiv)

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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References · 10

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 656
  4. 2021SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processingcited by 180
  5. 2020Improved Noisy Student Training for Automatic Speech Recognitioncited by 141
  6. 2020LRSpeech: Extremely Low-Resource Speech Synthesis and Recognitioncited by 43
  7. 2019Almost Unsupervised Text to Speech and Automatic Speech Recognitioncited by 41
  8. 2021The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoderscited by 32
  9. 2023Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentationcited by 31
  10. 2023A Novel Self-training Approach for Low-resource Speech Recognitioncited by 7

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