# Text-Based Detection of On-Hold Scripts in Contact Center Calls

arXiv: https://arxiv.org/abs/2407.09849 · HTML: https://mlsanity.com/paper/2407.09849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 13 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data](https://mlsanity.com/paper/1606.08698/) (2016)
- [Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting](https://mlsanity.com/paper/2206.15400/) (2022)
- [Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding](https://mlsanity.com/paper/2308.06472/) (2023)
- [Open-vocabulary Keyword-spotting with Adaptive Instance Normalization](https://mlsanity.com/paper/2309.08561/) (2023)
- [Predicting Customer Call Intent by Analyzing Phone Call Transcripts based on CNN for Multi-Class Classification](https://mlsanity.com/paper/1907.03715/) (2019)

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

- [Conversational Rubert for Detecting Competitive Interruptions in ASR-Transcribed Dialogues](https://mlsanity.com/paper/2407.14940/) (2024)

## Abstract (arXiv)

Average hold time is a concern for call centers because it affects customer satisfaction. Contact centers should instruct their agents to use special on-hold scripts to maintain positive interactions with clients. This study presents a natural language processing model that detects on-hold phrases in customer service calls transcribed by automatic speech recognition technology. The task of finding hold scripts in dialogue was formulated as a multiclass text classification problem with three mutually exclusive classes: scripts for putting a client on hold, scripts for returning to a client, and phrases irrelevant to on-hold scripts. We collected an in-house dataset of calls and labeled each dialogue turn in each call. We fine-tuned RuBERT on the dataset by exploring various hyperparameter sets and achieved high model performance. The developed model can help agent monitoring by providing a way to check whether an agent follows predefined on-hold scripts.