AI-Based IVR
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Abstract (arXiv)
The use of traditional IVR (Interactive Voice Response) methods often proves insufficient to meet customer needs. This article examines the application of artificial intelligence (AI) technologies to enhance the efficiency of IVR systems in call centers. A proposed approach is based on the integration of speech-to-text conversion solutions, text query classification using large language models (LLM), and speech synthesis. Special attention is given to adapting these technologies to work with the Kazakh language, including fine-tuning models on specialized datasets. The practical aspects of implementing the developed system in a real call center for query classification are described. The research results demonstrate that the application of AI technologies in call center IVR systems reduces operator workload, improves customer service quality, and increases the efficiency of query processing. The proposed approach can be adapted for use in call centers operating with various languages.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 20 Aug 2024.
Builds on
3 of 9 references.
References · 3
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023Scaling Speech Technology to 1,000+ Languagescited by 411
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Citing papers per year: 2025: 1.
1 paper.
- 2025Beyond IVR Touch-Tones: Customer Intent Routing using LLMs14 Sep 2025