Abstract
We introduce a low-latency telecom AI voice agent pipeline for real-time, interactive telecommunications use, enabling advanced voice AI for call center automation, intelligent IVR (Interactive Voice Response), and AI-driven customer support. The solution is built for telecom, combining four specialized models by NetoAI: TSLAM, a 4-bit quantized Telecom-Specific Large Language Model (LLM); T-VEC, a Telecom-Specific Embedding Model; TTE, a Telecom-Specific Automatic Speech Recognition (ASR) model; and T-Synth, a Telecom-Specific Text-to-Speech (TTS) model. These models enable highly responsive, domain-adapted voice AI agents supporting knowledge-grounded spoken interactions with low latency. The pipeline integrates streaming ASR (TTE), conversational intelligence (TSLAM), retrieval augmented generation (RAG) over telecom documents, and real-time TTS (T-Synth), setting a new benchmark for telecom voice assistants. To evaluate the system, we built a dataset of 500 human-recorded telecom questions from RFCs, simulating real telecom agent queries. This framework allows analysis of latency, domain relevance, and real-time performance across the stack. Results show that TSLAM, TTE, and T-Synth deliver real-time factors (RTF) below 1.0, supporting enterprise, low-latency telecom deployments. These AI agents -- powered by TSLAM, TTE, and T-Synth -- provide a foundation for next-generation telecom AI, enabling automated customer support, diagnostics, and more.
Abstract (translated)
我们介绍了一种低延迟的电信AI语音代理管道，适用于实时互动通信场景。该解决方案旨在通过实现呼叫中心自动化、智能IVR（交互式语音应答）和基于人工智能的客户支持来推动先进语音AI的应用。 此方案由NetoAI开发的四个专业模型构成：TSLAM是一个4位量化版本的专门针对电信领域的大型语言模型；T-VEC是专门的嵌入模型；TTE是专门的自动语音识别（ASR）模型；而T-Synth则是专门的文本转语音（TTS）模型。这些模型结合在一起，能够支持快速响应、领域特定调整的语音AI代理，实现基于知识的基础对话交互，并具有低延迟的特点。 该管道集成了流式ASR（TTE）、会话智能（TSLAM）、电信文档上的检索增强生成（RAG），以及实时TTS（T-Synth），为电信语音助手设定了新的性能标准。为了评估这套系统，我们建立了一个包含500个由真人录制的电信问题的数据集，这些问题均源自RFCs，用于模拟真实的电信代理查询场景。这一框架能够全面分析延迟、领域相关性以及整个堆栈中的实时性能。 实验结果表明，TSLAM、TTE和T-Synth在实现实时因子（RTF）低于1.0的同时，支持企业级的低延迟能力。这些由TSLAM、TTE和T-Synth驱动的AI代理为下一代电信AI奠定了基础，推动了自动化客户支持、故障诊断等领域的进步和发展。
URL
https://arxiv.org/abs/2508.04721
