arXiv Computation and Language

InteractionBench: Measuring Timing, Task Accuracy, Whisper and Volume, and Grounding in Real-Time Voice Systems

InteractionBench is a benchmark that evaluates real‑time voice systems on four dimensions—conversational timing, spoken‑task accuracy, whispering and volume, and groundedness—using the same recorded user audio across thirteen scenarios. It tests seven systems (GPT‑Live, GPT‑Realtime, Gemini Live, Moshi, PersonaPlex, Freeze‑Omni, and a turn‑based cascade) over 1,519 runs, revealing correlations between timing and task accuracy and highlighting differences in whispering, grammar correction, and groundedness performance. The study also notes protocol variations and the impact of network delay on timing scores.

arXiv Computation and Language
Oct 12

People Making Conversation: Evaluating How Naturally Speech Models Converse

The study evaluates conversational speech models by placing them at 427 pauses and 482 turn ends from 176 unscripted conversations, scoring their responses on timing, latency, length, wording, and recall of recent dialogue. Hosted APIs (GPT Realtime, Gemini 3.1 Flash Live, Grok Voice) respond at almost every pause, with latency ranging from 1–4 s for silence-based endpointers and about 8 s for semantic turn detection, while full‑duplex models (PersonaPlex, Moshi) remain silent more often and can interrupt within human reaction time. Recall rates vary: hosted systems remember 41–60 % of facts under a Claude Opus 5.5 judge and 19–26 % with a strict word matcher; full‑duplex models recall 17–22 % and 12–15 % respectively.

By Arya Bastani, Iman Modarressi
arXiv Computer Vision
Sep 15

Realtime-Venus: A full-duplex interaction system with asynchronous delegation

arXiv:2609.13814v1 Announce Type: new Abstract: Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and li...

By Ruixiang Zhao, Hualei Wang, Renhe Sun, Enzhi Zhou, Jincenzi Wu, Xujie Song, Kexin Shi, Zihang Liu, Pengcheng Zhu, Jiayi Zhou, Baoyue Zhang, Changhao Zhang, Zitong Wang, Jinhong Wang, Tong Niu, Jingjing Liu, Junan Lin, Haolin He, Hengshuo Chu, Yuhui Chen, Jian Liu, Yuge Huang, Junliang Xing, Yuntao Wang, Weiqiang Wang, Chun Yu, Yuanchun Shi
arXiv AI
Sep 4

DuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents

DuplexSpeechBench-IFEval (DSB-IFEval) is a new benchmark that evaluates how full‑duplex voice agents follow implicit instructions during real‑time spoken interaction. It contains 1,038 test cases across eight assistant roles and tests five conditioning protocols, measuring floor management with an Instruction Adherence Score (IAS) and persona consistency with a Persona Adherence Score (PAS). Experiments on six speech systems reveal architecture‑dependent trade‑offs, showing that some models are more sensitive to explicit versus persona‑only instructions and that even when following conflicting directives, they struggle to override them under safety conflict.

By Puneet Mathur, Dinesh Manocha
arXiv AI
Sep 11

RelayS2S: A Dual-Path Speculative Generation for Real-Time Dialogue

RelayS2S is a hybrid real‑time dialogue system that runs a fast duplex speech‑to‑speech path and a slow ASR‑to‑LLM path in parallel. The fast path speculatively drafts a short response prefix and streams it to TTS, while the slow path generates a higher‑quality continuation conditioned on that prefix. A lightweight verifier decides whether to commit the prefix or fall back to the cascaded pipeline, achieving much lower latency (81 ms P90 first‑chunk) while preserving 99% of the cascaded pipeline’s textual quality.

By Long Mai, Junli Liang
arXiv AI
Sep 21

NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model with Tool Calling Capabilities

arXiv:2609.21967v1 Announce Type: cross Abstract: We introduce NemotronLabs VoiceChat, an open full-duplex speech-to-speech model with native tool-calling capabilities. NemotronLabs VoiceChat combine...

By Jagadeesh Balam, Travis Bartley, Edresson Casanova, Sanjay Chauhan, Chen Chen, Zhehuai Chen, Zijia Chen, Francesco Ciannella, Slyne Deng, Mikyas Desta, Harishchandra Dubey, Slim Essid, Nourchene Ferchichi, Boris Ginsburg, Mariana Graterol Fuenmayor, Negar Habibi, Kevin Hu, Anand Joseph, Viraj Karandikar, Myungjong Kim, Viacheslav Klimkov, Seelan Lakshmi Narasimhan, Lily Lee, Jason Li, Eileen Long, Ameya Mahabaleshwarkar, Aditya Malte, Adi Margolin, Sasha Meister, Valentin Mendelev, Oluwatobi Olabiyi, Ankita Pasad, Yifan Peng, Elena Rastorgueva, Jayda Ritchie, Jason Roche, Nikhil Srihari, Yuanhang Su, Yoshi Suhara, Viet Anh Trinh, Jinhan Wang, Piotr Zelasko, Hui Wang, Puhui Meng, Chaosen Zhang, Yunsheng Liu, Shawn Wang, Wenjing Li, Zhonglei He
arXiv Computation and Language
Sep 16

ECHO: A Matched-Contrast Benchmark for Context-Sensitive Turn-Taking in Full-Duplex Dialogue

ECHO is a new paired diagnostic benchmark designed for Chinese full‑duplex spoken dialogue systems to evaluate context‑sensitive turn‑taking. It pairs examples that share the same overlap transcript but differ in preceding multi‑turn context, requiring either a Yield or Keep decision, and also includes off‑talk cases to test unnecessary yielding. The benchmark introduces pair accuracy, penalizing constant‑action policies, and shows that many systems are biased toward Yield, performing better on interruptions than on backchannels.

By Shuofeng Zhao, Hongwei Cai, Wenke Fan, Qingxiang Guo, Dawei Yang, Zhou Wang, Zhiyang Zhou, Yingxin Shang, Weixu Wang, Lin Yang, Shuran Zhou, Yang Song
arXiv AI
Oct 2

DuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents

DuplexSpeechBench-Document Grounding (DSB‑DG) is a benchmark that evaluates how well voice agents can ground their responses in external documents across five professional domains. It focuses on three failure modes—Context Saturation, Grounding Decay, and Proactive Grounding—and includes 1,636 adversarially verified QA pairs from 50 documents. The benchmark measures grounding accuracy, hallucination, and response latency, revealing that cascaded pipelines achieve the highest accuracy while open‑weight systems suffer from abrupt capacity collapse and multi‑turn decay.

By Puneet Mathur, Nedim Lipka, Zeyu Jin, Dinesh Manocha