# AI-Based IVR

arXiv: https://arxiv.org/abs/2408.10549 · HTML: https://mlsanity.com/paper/2408.10549/

## Key facts

- Cited by 1 papers 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 (most cited first, 3 of 3)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)

## Built on by (most cited first, 1 of 1)

- [Beyond IVR Touch-Tones: Customer Intent Routing using LLMs](https://mlsanity.com/paper/2510.21715/) (2025)

## 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.