Summary of Ai-based Ivr, by Gassyrbek Kosherbay et al.
AI-Based IVR
by Gassyrbek Kosherbay, Nurgissa Apbaz
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the integration of artificial intelligence (AI) technologies to enhance the efficiency of Interactive Voice Response (IVR) systems in call centers. A proposed approach combines speech-to-text conversion, large language models (LLM) for text query classification, and speech synthesis. The focus is on adapting these technologies to work with the Kazakh language, including fine-tuning models on specialized datasets. The practical implementation of the system in a real call center for query classification is discussed. The results demonstrate that AI-powered IVR systems reduce operator workload, improve customer service quality, and increase query processing efficiency. This approach can be adapted for use in multilingual call centers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes IVR systems better by using artificial intelligence (AI). IVR lets people talk to computers over the phone, but sometimes it’s not good enough. The researchers tried combining different AI tools like speech-to-text and language models to make IVR faster and more helpful. They tested this on the Kazakh language, which is special because there aren’t many computer programs that can understand it well. By making IVR better, people won’t have to wait as long for help, and customer service will be improved. |
Keywords
» Artificial intelligence » Classification » Fine tuning
