Cloning a Conversational Voice AI Agent from Call\,Recording Datasets for Telesales

• URL: http://arxiv.org/abs/2509.04871v1
• Date: Fri, 05 Sep 2025 07:36:12 GMT
• Title: Cloning a Conversational Voice AI Agent from Call\,Recording Datasets for Telesales
• Authors: Krittanon Kaewtawee, Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn,
• Abstract summary: We present a methodology for cloning a conversational voice AI agent from a corpus of call recordings.<n>Our system listens to customers over the telephone, responds with a synthetic voice, and follows a structured playbook learned from top performing human agents.<n>The cloned agent is evaluated against human agents on a rubric of 22 criteria covering introduction, product communication, sales drive, objection handling, and closing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in language and speech modelling have made it possible to build autonomous voice assistants that understand and generate human dialogue in real time. These systems are increasingly being deployed in domains such as customer service and healthcare care, where they can automate repetitive tasks, reduce operational costs, and provide constant support around the clock. In this paper, we present a general methodology for cloning a conversational voice AI agent from a corpus of call recordings. Although the case study described in this paper uses telesales data to illustrate the approach, the underlying process generalizes to any domain where call transcripts are available. Our system listens to customers over the telephone, responds with a synthetic voice, and follows a structured playbook learned from top performing human agents. We describe the domain selection, knowledge extraction, and prompt engineering used to construct the agent, integrating automatic speech recognition, a large language model based dialogue manager, and text to speech synthesis into a streaming inference pipeline. The cloned agent is evaluated against human agents on a rubric of 22 criteria covering introduction, product communication, sales drive, objection handling, and closing. Blind tests show that the AI agent approaches human performance in routine aspects of the call while underperforming in persuasion and objection handling. We analyze these shortcomings and refine the prompt accordingly. The paper concludes with design lessons and avenues for future research, including large scale simulation and automated evaluation.

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