| {"slug":"turnbench","name":"TurnBench","maintainer":"Sesame AI Labs / Mundo AI / CMU / oto","tagline":"Scores end-of-turn and interruption detection on 30 hours of hand-labelled dyadic conversation, with conversation type as a controlled variable.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["end-of-turn recall","interruption recall","false-positive rate","median detection latency"],"models_scored":3,"updated":"2026-11","url":"https://turnbench.sesame.com/","paper_url":"https://arxiv.org/abs/2608.25218","leaderboard_url":"https://turnbench.sesame.com/","page_url":"https://www.fullduplex.ai/benchmarks/turnbench"} | |
| {"slug":"fdb-v1","name":"Full-Duplex-Bench v1","maintainer":"NTU / UC Berkeley / UW / MIT","tagline":"Original Full-Duplex-Bench — automatic scoring of the four canonical turn-taking events: when-to-speak, back-channel, interruption success, pause handling.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["turn-taking latency","back-channel timing","interruption success","pause handling"],"models_scored":5,"updated":"2025-03","url":"https://github.com/DanielLin94144/Full-Duplex-Bench","paper_url":"https://arxiv.org/abs/2503.04721","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/fdb-v1"} | |
| {"slug":"fdb-v15","name":"Full-Duplex-Bench v1.5","maintainer":"NTU / UC Berkeley / UW / MIT","tagline":"Mid-2025 refresh of FDB v1 — adds the simultaneous-speech / overlap axis (user interruption, listener back-channel, side conversation, ambient speech).","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["overlap duration","mutual-silence rate","interruption recovery"],"models_scored":3,"updated":"2025-08","url":"https://github.com/DanielLin94144/Full-Duplex-Bench","paper_url":"https://arxiv.org/abs/2507.23159","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/fdb-v15"} | |
| {"slug":"fdb-v2","name":"Full-Duplex-Bench v2","maintainer":"NTU / UC Berkeley / UW / MIT","tagline":"Late-2025 WebRTC-based live harness — first FDB release that runs dynamic evaluation against streaming endpoints, plus multi-turn stimuli.","full_duplex":true,"tier":"native","setting":"live","focus":["full-duplex","speech-lm"],"metrics":["streaming latency","multi-turn coherence","overlap handling"],"models_scored":0,"updated":"2026-02","url":"https://github.com/DanielLin94144/Full-Duplex-Bench","paper_url":"https://arxiv.org/abs/2510.07838","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/fdb-v2"} | |
| {"slug":"fdb-v3","name":"Full-Duplex-Bench v3","maintainer":"NTU (NVIDIA advisory)","tagline":"Multi-step tool calling for voice agents, driven by real human speech with natural disfluencies.","full_duplex":true,"tier":"native","setting":"live","focus":["full-duplex","vertical-agent"],"metrics":["Pass@1","tool-call F1","response latency","turn-take rate"],"models_scored":2,"updated":"2026-05","url":"https://github.com/DanielLin94144/Full-Duplex-Bench","paper_url":"https://arxiv.org/abs/2604.04847","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/fdb-v3"} | |
| {"slug":"videofdb","name":"VideoFDB","maintainer":"NVIDIA / David AI","tagline":"Full-duplex benchmark for video calls: does the agent read the user's nonverbal cues, and do its own voice and face respond the way a person's would?","full_duplex":true,"tier":"native","setting":"live","focus":["full-duplex"],"metrics":["perception rubric (0 to 5)","generation rubric (0 to 5)","TOR-Alignment","median response latency"],"models_scored":4,"updated":"2026-10","url":"https://research.nvidia.com/labs/amri/projects/video-fdb/","paper_url":"https://arxiv.org/abs/2605.30256","leaderboard_url":"https://research.nvidia.com/labs/amri/projects/video-fdb/leaderboard.html","page_url":"https://www.fullduplex.ai/benchmarks/videofdb"} | |
| {"slug":"duplexact","name":"DuplexAct","maintainer":"Dong et al.","tagline":"Scores whether full-duplex speech models take the right interaction action when a conversation calls for it, and whether they follow through.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["immediate execution success","follow-through success"],"models_scored":8,"updated":"2026-10","url":"https://github.com/JiahengDong/DuplexAct","paper_url":"https://arxiv.org/abs/2610.17738","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/duplexact"} | |
| {"slug":"multipartyturnbench","name":"MultiPartyTurnBench","maintainer":"Mathur and Manocha","tagline":"Floor control with two to five speakers: can a detector or full-duplex agent tell a real turn change from a hold, a backchannel, or speech that does not take the floor?","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["end-of-turn recall at fixed FPR","interruption recall at fixed FPR","signed latency","selective interruption balanced accuracy"],"models_scored":2,"updated":"2026-10","url":"https://multipartyturnbench.github.io/","paper_url":"https://arxiv.org/abs/2610.20172","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/multipartyturnbench"} | |
| {"slug":"multitalkbench","name":"MultiTalkBench","maintainer":"Wang et al. (MultiTalk)","tagline":"Long, multi-party, English and Chinese full-duplex dialogue, with conversations that average 32.6 minutes.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["long-range entity tracking","topic coherence","addressee selection","turn-taking"],"models_scored":0,"updated":"2026-09","url":"https://huggingface.co/datasets/MultiTalk/MultiTalkBench","paper_url":"https://arxiv.org/abs/2609.36903","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/multitalkbench"} | |
| {"slug":"m3-duplexbench","name":"M3-DuplexBench","maintainer":"NTT (Fukuda et al.)","tagline":"English and Japanese full-duplex benchmark that scores interaction timing and response content together, in casual chat and multi-turn QA.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","speech-lm"],"metrics":["turn-taking rate and latency","pause handling","backchannel and barge-in response","response content"],"models_scored":0,"updated":"2026-10","url":"https://github.com/nttcslab-sp/m3-duplexbench","paper_url":"https://arxiv.org/abs/2607.29125","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/m3-duplexbench"} | |
| {"slug":"duplex-mpe","name":"Duplex-MPE","maintainer":"Ma et al.","tagline":"Multi-party full-duplex test: with three or four people talking, should the assistant answer, stay silent or stop?","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["response initiation","answer accuracy","silence preservation","stopping when resolved"],"models_scored":0,"updated":"2026-11","url":"https://step-out.github.io/Duplex-MPE-Page/","paper_url":"https://arxiv.org/abs/2609.31948","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/duplex-mpe"} | |
| {"slug":"duplexledger","name":"DuplexLedger","maintainer":"Li et al.","tagline":"Does a full-duplex agent remember the right things? Write, update, ignore and retain decisions during live interaction.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","speech-lm"],"metrics":["latest-value accuracy","final-state fact coverage","stale assertions","distractor adoption"],"models_scored":0,"updated":"2026-10","url":"https://github.com/lyx-researcher/DuplexLedger","paper_url":"https://arxiv.org/abs/2610.27581","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/duplexledger"} | |
| {"slug":"interactionbench","name":"InteractionBench","maintainer":"Avera Labs (He et al.)","tagline":"Timing, spoken-task accuracy, whisper and volume, and groundedness over 20+ turns, measured on the same recorded user audio for every system.","full_duplex":true,"tier":"native","setting":"live","focus":["full-duplex","speech-lm"],"metrics":["conversational timing","spoken-task accuracy","whisper and volume control","groundedness"],"models_scored":0,"updated":"2026-11","url":"https://avera-labs.github.io/InteractionBench/","paper_url":"https://arxiv.org/abs/2611.02430","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/interactionbench"} | |
| {"slug":"echo-turn-taking","name":"ECHO","maintainer":"Zhao et al.","tagline":"Paired Chinese turn-taking test: the same overlapping words must sometimes take the floor and sometimes not, depending on context.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["pair accuracy","yield accuracy","keep accuracy","off-talk false yields"],"models_scored":0,"updated":"2026-09","url":"https://huggingface.co/datasets/shuofeng123/ECHO","paper_url":"https://arxiv.org/abs/2609.17360","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/echo-turn-taking"} | |
| {"slug":"mp-bench","name":"MP-Bench","maintainer":"Shih et al.","tagline":"The voice agent is one participant in a spoken group conversation: does it follow who said what, and does it take its turn?","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","speech-lm"],"metrics":["turn-taking accuracy","response appropriateness","comprehension accuracy","latency"],"models_scored":0,"updated":"2026-10","url":"https://github.com/atosystem/MP-Bench","paper_url":"https://arxiv.org/abs/2609.13076","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/mp-bench"} | |
| {"slug":"dsb-ifeval","name":"DuplexSpeechBench-IFEval","maintainer":"Mathur and Manocha","tagline":"Does a full-duplex agent infer from its persona when to speak, backchannel, interrupt or yield, and not only from explicit rules?","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","instruction-following"],"metrics":["Instruction Adherence Score","Persona Adherence Score"],"models_scored":0,"updated":"2026-09","url":"https://dsb-ifeval.github.io/","paper_url":"https://arxiv.org/abs/2609.03423","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/dsb-ifeval"} | |
| {"slug":"talking-turns","name":"Talking Turns","maintainer":"Apple / CMU","tagline":"A supervised turn-taking judge trained on human-human conversations to score audio foundation models.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","speech-lm"],"metrics":["turn-event F1","backchannel rate","interruption rate"],"models_scored":3,"updated":"2025-04","url":"https://machinelearning.apple.com/research/talking-turns","paper_url":"https://arxiv.org/abs/2503.01174","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/talking-turns"} | |
| {"slug":"voicebench","name":"VoiceBench","maintainer":"NUS","tagline":"Evaluates LLM-based voice assistants across knowledge, reasoning, safety, and instruction-following — judged by a GPT-4-class model.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm","instruction-following"],"metrics":["overall","QA","reasoning (BBH)","IFEval","safety"],"models_scored":7,"updated":"2025-06","url":"https://github.com/MatthewCYM/VoiceBench","paper_url":"https://arxiv.org/abs/2410.17196","leaderboard_url":"https://matthewcym.github.io/VoiceBench/","page_url":"https://www.fullduplex.ai/benchmarks/voicebench"} | |
| {"slug":"vocalbench","name":"VocalBench","maintainer":"SJTU (OmniAgent)","tagline":"A four-dimensional stress test for speech-interaction models — semantic, acoustic, conversational, and robust.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm","audio-understanding"],"metrics":["ACC","LLM-judge","UTMOS","WER","EER"],"models_scored":0,"updated":"2026-01","url":"https://github.com/SJTU-OmniAgent/VocalBench","paper_url":"https://arxiv.org/abs/2505.15727","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/vocalbench"} | |
| {"slug":"uro-bench","name":"URO-Bench","maintainer":"Ruiqi Yan et al.","tagline":"The first end-to-end S2S benchmark with multilingual, multi-turn, and paralinguistic coverage.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm","instruction-following","emotion"],"metrics":["U/R/O composite","paralinguistic score"],"models_scored":2,"updated":"2025-08","url":"https://github.com/Ruiqi-Yan/URO-Bench","paper_url":"https://arxiv.org/abs/2502.17810","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/uro-bench"} | |
| {"slug":"air-bench","name":"AIR-Bench","maintainer":"Alibaba DAMO (OFA-Sys)","tagline":"A two-tier stress test for audio-LLMs covering speech, natural sound, and music — MCQ plus chat.","full_duplex":false,"tier":"native","setting":"lab","focus":["audio-understanding","instruction-following"],"metrics":["MCQ acc","GPT-4 judge score"],"models_scored":3,"updated":"2024-12","url":"https://github.com/OFA-Sys/AIR-Bench","paper_url":"https://arxiv.org/abs/2402.07729","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/air-bench"} | |
| {"slug":"big-bench-audio","name":"Big Bench Audio","maintainer":"Artificial Analysis","tagline":"1 000 audio-question reasoning benchmark — the dataset behind the Artificial Analysis S2S speech-reasoning leaderboard.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm","instruction-following"],"metrics":["accuracy","TTFA (s)"],"models_scored":7,"updated":"2026-05","url":"https://huggingface.co/datasets/ArtificialAnalysis/big_bench_audio","paper_url":null,"leaderboard_url":"https://artificialanalysis.ai/speech-to-speech","page_url":"https://www.fullduplex.ai/benchmarks/big-bench-audio"} | |
| {"slug":"aa-speech-to-speech-index","name":"Artificial Analysis Speech to Speech Index","maintainer":"Artificial Analysis","tagline":"Composite score for native speech-to-speech models: speech reasoning, agentic task completion, human preference and task success, weighted equally.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm","vertical-agent"],"metrics":["index score","Big Bench Audio accuracy","τ-Voice pass rate","Arena Score","task success rate"],"models_scored":0,"updated":"2026-08","url":"https://artificialanalysis.ai/methodology/speech-to-speech-benchmarking","paper_url":null,"leaderboard_url":"https://artificialanalysis.ai/speech-to-speech","page_url":"https://www.fullduplex.ai/benchmarks/aa-speech-to-speech-index"} | |
| {"slug":"mmar","name":"MMAR","maintainer":"SJTU / MBZUAI · NeurIPS 2025","tagline":"1 000 human-curated deep-reasoning audio QA items spanning speech, sound, music, and their mix — four hierarchical reasoning layers.","full_duplex":false,"tier":"component","setting":"lab","focus":["audio-understanding","speech-lm"],"metrics":["overall accuracy","per-layer accuracy","rubric score"],"models_scored":2,"updated":"2026-02","url":"https://github.com/ddlBoJack/MMAR","paper_url":"https://arxiv.org/abs/2505.13032","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/mmar"} | |
| {"slug":"ifeval-audio","name":"IFEval-Audio","maintainer":"AudioLLMs (I2R Singapore)","tagline":"Format-constrained spoken instructions, verified programmatically — an audio port of text IFEval.","full_duplex":false,"tier":"native","setting":"lab","focus":["instruction-following","speech-lm"],"metrics":["IFR","SCR","OSR"],"models_scored":0,"updated":"2025-05","url":"https://github.com/AudioLLMs/AudioBench/tree/main/IFEval-Audio","paper_url":"https://arxiv.org/abs/2505.16774","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/ifeval-audio"} | |
| {"slug":"scale-voice-showdown","name":"Scale Voice Showdown","maintainer":"Scale AI","tagline":"First in-the-wild preference arena for voice AI, launched March 2026 with 11 frontier models, 60+ languages and real user speech. The public leaderboard is offline as of October 2026.","full_duplex":false,"tier":"native","setting":"arena","focus":["speech-lm","tts"],"metrics":["preference win-rate","per-language score"],"models_scored":1,"updated":"2026-03","url":"https://labs.scale.com/","paper_url":null,"leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/scale-voice-showdown"} | |
| {"slug":"aa-speech-arena","name":"Artificial Analysis Speech Arena","maintainer":"Artificial Analysis","tagline":"Blind user votes turn TTS models into a live Elo leaderboard — the de-facto preference number for TTS. Despite the 'Speech Arena' label, this is a TTS-only (text → speech) arena, not an STS ranking.","full_duplex":false,"tier":"adjacent","setting":"arena","focus":["tts"],"metrics":["Elo"],"models_scored":0,"updated":"2026-10","url":"https://artificialanalysis.ai/text-to-speech/arena","paper_url":null,"leaderboard_url":"https://artificialanalysis.ai/text-to-speech/leaderboard","page_url":"https://www.fullduplex.ai/benchmarks/aa-speech-arena"} | |
| {"slug":"aa-speech-agent-arena","name":"Artificial Analysis Speech Agent Arena","maintainer":"Artificial Analysis","tagline":"Human participants complete the same task with two hidden speech-to-speech systems over live calls, then vote for the conversation they preferred.","full_duplex":false,"tier":"native","setting":"arena","focus":["speech-lm","vertical-agent"],"metrics":["preference Elo","agentic Elo","non-agentic Elo","task success rate"],"models_scored":0,"updated":"2026-10","url":"https://artificialanalysis.ai/speech-to-speech/arena/overview","paper_url":null,"leaderboard_url":"https://artificialanalysis.ai/speech-to-speech/arena","page_url":"https://www.fullduplex.ai/benchmarks/aa-speech-agent-arena"} | |
| {"slug":"rw-voice-eq","name":"Real World Voice EQ Bench","maintainer":"Hume AI","tagline":"Human-rated leaderboards across TTS, speech-to-speech, speech understanding and ASR, built to test whether systems use what is in the audio and not only the words.","full_duplex":false,"tier":"component","setting":"arena","focus":["tts","speech-lm","asr","emotion"],"metrics":["rater score (1 to 5) per factor","WER (ASR board)"],"models_scored":0,"updated":"2026-07","url":"https://huggingface.co/spaces/HumeAI/rw-voice-eq","paper_url":"https://arxiv.org/abs/2607.14846","leaderboard_url":"https://huggingface.co/spaces/HumeAI/rw-voice-eq","page_url":"https://www.fullduplex.ai/benchmarks/rw-voice-eq"} | |
| {"slug":"mtalk-bench","name":"MTalk-Bench","maintainer":"FreedomIntelligence / CUHK-SZ","tagline":"First multi-turn S2S benchmark with both arena-style (pairwise) and rubric-based (absolute) protocols for dialogue evaluation.","full_duplex":false,"tier":"native","setting":"arena","focus":["speech-lm","instruction-following","emotion"],"metrics":["arena win-rate","rubric score","per-dimension score"],"models_scored":0,"updated":"2025-09","url":"https://github.com/FreedomIntelligence/MTalk-Bench","paper_url":"https://arxiv.org/abs/2508.18240","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/mtalk-bench"} | |
| {"slug":"tts-arena","name":"TTS Arena","maintainer":"Hugging Face","tagline":"The original blind A/B voting space for TTS, feeding an Elo ladder on Hugging Face.","full_duplex":false,"tier":"adjacent","setting":"arena","focus":["tts"],"metrics":["Elo"],"models_scored":0,"updated":"2026-03","url":"https://huggingface.co/spaces/TTS-AGI/TTS-Arena-V2","paper_url":null,"leaderboard_url":"https://huggingface.co/spaces/TTS-AGI/TTS-Arena-V2","page_url":"https://www.fullduplex.ai/benchmarks/tts-arena"} | |
| {"slug":"tau3-bench","name":"τ-bench 2.0 (τ-Voice)","maintainer":"Sierra Research","tagline":"The first agent benchmark with a live, full-duplex voice track, now in six languages with version 2.0.","full_duplex":true,"tier":"native","setting":"vertical","focus":["vertical-agent","full-duplex","speech-lm"],"metrics":["pass^k","tool-use success","voice task success","per-language task completion"],"models_scored":3,"updated":"2026-10","url":"https://sierra.ai/blog/bench-advancing-agent-benchmarking-to-knowledge-and-voice","paper_url":"https://arxiv.org/abs/2603.13686","leaderboard_url":"https://www.taubench.com","page_url":"https://www.fullduplex.ai/benchmarks/tau3-bench"} | |
| {"slug":"eva-bench","name":"EVA-Bench","maintainer":"ServiceNow","tagline":"End-to-end voice-agent evaluation with bot-to-bot calls, separate accuracy and experience scores, and accent and noise perturbations.","full_duplex":false,"tier":"native","setting":"vertical","focus":["vertical-agent","speech-lm"],"metrics":["EVA-A (accuracy)","EVA-X (experience)","pass@k and pass^k","accent and noise robustness"],"models_scored":1,"updated":"2026-10","url":"https://servicenow.github.io/eva/","paper_url":"https://arxiv.org/abs/2605.13841","leaderboard_url":"https://servicenow.github.io/eva/","page_url":"https://www.fullduplex.ai/benchmarks/eva-bench"} | |
| {"slug":"vamos-bench","name":"VAmoS Bench","maintainer":"Veris AI","tagline":"Simulated phone calls graded on what the voice agent said and on what it actually changed in the backend.","full_duplex":false,"tier":"native","setting":"vertical","focus":["vertical-agent"],"metrics":["task completion","latency","barge-ins per call","cost per call"],"models_scored":0,"updated":"2026-10","url":"https://veris.ai/leaderboard/vamos","paper_url":"https://arxiv.org/abs/2609.38512","leaderboard_url":"https://veris.ai/leaderboard/vamos-pro","page_url":"https://www.fullduplex.ai/benchmarks/vamos-bench"} | |
| {"slug":"apex-voice","name":"APEX-Voice","maintainer":"Mathur and Manocha","tagline":"Can a full-duplex voice agent finish professional work, such as form completion or negotiation, while the user interrupts and corrects?","full_duplex":true,"tier":"native","setting":"vertical","focus":["vertical-agent","full-duplex"],"metrics":["Pass@1","Reliable@3","artifact field accuracy"],"models_scored":0,"updated":"2026-09","url":"https://apex-voice.github.io/","paper_url":"https://arxiv.org/abs/2609.34973","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/apex-voice"} | |
| {"slug":"voiceagenteval","name":"VoiceAgentEval","maintainer":"Meituan / Xbench / Agora","tagline":"A dual-dimensional benchmark for expert-level outbound-calling agents — interaction fluency vs task flow compliance.","full_duplex":false,"tier":"native","setting":"vertical","focus":["vertical-agent","speech-lm"],"metrics":["GIC","TFC","task success"],"models_scored":0,"updated":"2025-10","url":"https://www.shengwang.cn/news/blogdetail/VoiceAgentEval/","paper_url":"https://arxiv.org/abs/2510.21244","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/voiceagenteval"} | |
| {"slug":"healthbench","name":"HealthBench","maintainer":"OpenAI","tagline":"5,000 multi-turn health conversations × 48k physician-written rubric criteria — text-only, listed as a transferable foundation rubric rather than a voice benchmark.","full_duplex":false,"tier":"adjacent","setting":"vertical","focus":["vertical-agent","instruction-following"],"metrics":["rubric score","per-theme breakdown"],"models_scored":0,"updated":"2025-05","url":"https://huggingface.co/datasets/openai/healthbench","paper_url":"https://arxiv.org/abs/2505.08775","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/healthbench"} | |
| {"slug":"seamless-expressive","name":"SeamlessExpressive (mExpresso)","maintainer":"Meta AI","tagline":"Expressive speech-to-speech translation judged on translation, prosody, and vocal-style preservation.","full_duplex":false,"tier":"component","setting":"lab","focus":["s2st"],"metrics":["ASR-BLEU","AutoPCP","Vocal Style Sim"],"models_scored":0,"updated":"2023-12","url":"https://github.com/facebookresearch/seamless_communication","paper_url":"https://arxiv.org/abs/2312.05187","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/seamless-expressive"} | |
| {"slug":"covost2","name":"CoVoST-2 BLEU","maintainer":"Meta AI","tagline":"21 language pairs of speech-to-text translation, long the S2T standard.","full_duplex":false,"tier":"component","setting":"lab","focus":["s2st","asr"],"metrics":["BLEU","chrF"],"models_scored":0,"updated":"2020-07","url":"https://github.com/facebookresearch/covost","paper_url":"https://arxiv.org/abs/2007.10310","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/covost2"} | |
| {"slug":"seed-tts-eval","name":"Seed-TTS Eval","maintainer":"ByteDance","tagline":"The de-facto evaluation set for zero-shot voice cloning — WER, speaker similarity, and CMOS in one recipe.","full_duplex":false,"tier":"component","setting":"lab","focus":["tts"],"metrics":["WER","SECS","CMOS"],"models_scored":0,"updated":"2024-11","url":"https://bytedancespeech.github.io/seedtts_tech_report/","paper_url":"https://arxiv.org/abs/2406.02430","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/seed-tts-eval"} | |
| {"slug":"nv-bench","name":"NV-Bench","maintainer":"Ni et al.","tagline":"Standardised test for generating laughs, sighs, fillers, and back-channels in TTS.","full_duplex":false,"tier":"component","setting":"lab","focus":["tts","emotion"],"metrics":["PCER","FAD","SIM","DNSMOS"],"models_scored":0,"updated":"2026-03","url":"https://arxiv.org/abs/2603.15352","paper_url":"https://arxiv.org/abs/2603.15352","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/nv-bench"} | |
| {"slug":"superb","name":"SUPERB","maintainer":"NTU et al. (s3prl)","tagline":"The classic: general-purpose speech representations scored on ten frozen-encoder downstream tasks.","full_duplex":false,"tier":"peripheral","setting":"lab","focus":["speech-lm","asr","audio-understanding"],"metrics":["composite score","per-task scores"],"models_scored":0,"updated":"2024-06","url":"https://github.com/s3prl/s3prl","paper_url":"https://arxiv.org/abs/2105.01051","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/superb"} | |
| {"slug":"librispeech","name":"LibriSpeech WER","maintainer":"OpenSLR","tagline":"The coordinate system of English ASR — test-clean and test-other WER.","full_duplex":false,"tier":"peripheral","setting":"lab","focus":["asr"],"metrics":["WER (test-clean)","WER (test-other)"],"models_scored":0,"updated":"2024-01","url":"https://www.openslr.org/12","paper_url":null,"leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/librispeech"} | |
| {"slug":"meld","name":"MELD / IEMOCAP","maintainer":"Academic","tagline":"The classic emotion-recognition pair, evaluated with full dialogue context.","full_duplex":false,"tier":"peripheral","setting":"lab","focus":["emotion","audio-understanding"],"metrics":["Weighted F1","Accuracy"],"models_scored":0,"updated":"2023-06","url":"https://affective-meld.github.io","paper_url":null,"leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/meld"} | |
| {"slug":"sid-bench","name":"SID-Bench","maintainer":"Xia et al.","tagline":"Semantic-aware interruption detection, built from real human dialogues and scored with a time penalty for both false alarms and late responses.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["Average Penalty Time (APT)","false alarms","response delay"],"models_scored":0,"updated":"2026-03","url":"https://github.com/xkx-hub/SID-bench","paper_url":"https://arxiv.org/abs/2603.24144","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/sid-bench"} | |
| {"slug":"fd-bench","name":"FD-Bench","maintainer":"Peng et al.","tagline":"A pipeline of LLM, TTS and ASR that simulates users and tests how full-duplex systems handle interruptions, delays and noise.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["interruption response","delay","robustness under noise"],"models_scored":0,"updated":"2025-07","url":"https://github.com/pengyizhou/FD-Bench","paper_url":"https://arxiv.org/abs/2507.19040","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/fd-bench"} | |
| {"slug":"mtr-duplex-bench","name":"MTR-DuplexBench","maintainer":"Zhang et al.","tagline":"Multi-round benchmark that splits continuous full-duplex dialogue into turns and scores each round.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","speech-lm"],"metrics":["conversational features","dialogue quality","instruction following","safety"],"models_scored":0,"updated":"2026-04","url":"https://github.com/ZhangHe0918/MTR-DuplexBench","paper_url":"https://arxiv.org/abs/2511.10262","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/mtr-duplex-bench"} | |
| {"slug":"humdial","name":"HumDial (ICASSP '26)","maintainer":"ICASSP 2026 Spoken Dialogue Challenge","tagline":"Dual-track human-evaluated spoken dialogue challenge — first public benchmark to explicitly score paralinguistic input alongside turn-taking dynamics.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","speech-lm","emotion"],"metrics":["turn-taking","paralinguistic grounding","human preference"],"models_scored":0,"updated":"2026-01","url":"https://aslp-lab.github.io/HumDial-Challenge/","paper_url":"https://arxiv.org/abs/2601.05564","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/humdial"} | |
| {"slug":"flexi","name":"FLEXI","maintainer":"Ge et al.","tagline":"Full-duplex benchmark that includes the model interrupting the user in emergencies, across six interaction scenarios.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex"],"metrics":["latency","response quality","emergency interruption"],"models_scored":0,"updated":"2025-09","url":"https://github.com/ChristineCHEN274/FLEXI","paper_url":"https://arxiv.org/abs/2509.22243","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/flexi"} | |
| {"slug":"audiobench","name":"AudioBench","maintainer":"AudioLLMs (I2R Singapore)","tagline":"Broad audio-understanding benchmark covering speech, music, and ambient sound reasoning.","full_duplex":false,"tier":"component","setting":"lab","focus":["audio-understanding"],"metrics":["per-track accuracy","overall"],"models_scored":0,"updated":"2024-07","url":"https://github.com/AudioLLMs/AudioBench","paper_url":"https://arxiv.org/abs/2406.16020","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/audiobench"} | |
| {"slug":"dynamic-superb","name":"Dynamic-SUPERB","maintainer":"NTU et al.","tagline":"Instruction-tuned successor to SUPERB, with 180 tasks in Phase-2 across speech understanding and light paralinguistic input.","full_duplex":false,"tier":"component","setting":"lab","focus":["speech-lm","instruction-following"],"metrics":["per-task accuracy","composite score"],"models_scored":0,"updated":"2024-11","url":"https://github.com/dynamic-superb/dynamic-superb","paper_url":"https://arxiv.org/abs/2411.05361","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/dynamic-superb"} | |
| {"slug":"voiceassistant-eval","name":"VoiceAssistant-Eval","maintainer":"Wang et al.","tagline":"10,497 examples in 13 task categories that test voice assistants on listening, speaking and viewing.","full_duplex":false,"tier":"component","setting":"lab","focus":["speech-lm","instruction-following"],"metrics":["content quality","speech quality","consistency"],"models_scored":0,"updated":"2025-09","url":"https://mathllm.github.io/VoiceAssistantEval/","paper_url":"https://arxiv.org/abs/2509.22651","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/voiceassistant-eval"} | |
| {"slug":"sd-eval","name":"SD-Eval","maintainer":"Academic (SD-Eval authors)","tagline":"Baseline probe on paralinguistic input — does a speech-LM use tone, emotion, and speaker traits at all?","full_duplex":false,"tier":"component","setting":"lab","focus":["speech-lm","emotion"],"metrics":["paralinguistic-grounded accuracy","delta over ASR baseline"],"models_scored":0,"updated":"2024-06","url":"https://arxiv.org/abs/2406.13340","paper_url":"https://arxiv.org/abs/2406.13340","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/sd-eval"} | |
| {"slug":"prosaudit","name":"ProsAudit","maintainer":"Academic (ProsAudit authors)","tagline":"Prosodic boundary detection benchmark — can the model perceive phrase / sentence boundaries from audio alone?","full_duplex":false,"tier":"component","setting":"lab","focus":["emotion","audio-understanding"],"metrics":["boundary F1","delta over text-only"],"models_scored":0,"updated":"2023-02","url":"https://arxiv.org/abs/2302.12057","paper_url":"https://arxiv.org/abs/2302.12057","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/prosaudit"} | |
| {"slug":"vocalbench-zh","name":"VocalBench-zh","maintainer":"SJTU (OmniAgent)","tagline":"Mandarin version of VocalBench — 10 subsets, ~10k instances, 14 models evaluated at the Nov 2025 launch.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm","audio-understanding"],"metrics":["ACC","LLM-judge","UTMOS","WER","EER"],"models_scored":0,"updated":"2025-11","url":"https://github.com/SJTU-OmniAgent/VocalBench-zh","paper_url":"https://arxiv.org/abs/2511.08230","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/vocalbench-zh"} | |
| {"slug":"cs3-bench","name":"CS3-Bench","maintainer":"Academic (CS3-Bench authors)","tagline":"Mandarin-English code-switching benchmark: across seven speech-to-speech models, knowledge-intensive question answering drops by up to 66% relative on code-switched input.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm"],"metrics":["monolingual-vs-codeswitch delta","per-pair accuracy"],"models_scored":0,"updated":"2025-10","url":"https://huggingface.co/datasets/VocalNet/CS3-Bench","paper_url":"https://arxiv.org/abs/2510.07881","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/cs3-bench"} | |
| {"slug":"j-moshi","name":"J-Moshi (subjective)","maintainer":"Ohashi et al.","tagline":"The first public Japanese full-duplex dialogue model, built on Moshi and rated by human listeners for naturalness and meaningfulness.","full_duplex":true,"tier":"native","setting":"lab","focus":["full-duplex","speech-lm"],"metrics":["naturalness","meaningfulness"],"models_scored":0,"updated":"2025-06","url":"https://nu-dialogue.github.io/j-moshi","paper_url":"https://arxiv.org/abs/2506.02979","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/j-moshi"} | |
| {"slug":"voiceagentbench","name":"VoiceAgentBench","maintainer":"Academic (VoiceAgentBench authors)","tagline":"Spoken agentic benchmark: 6,000+ synthetic spoken queries covering single-tool calls, multi-tool workflows, multi-turn dialogue and safety, in English and six Indic languages.","full_duplex":false,"tier":"native","setting":"vertical","focus":["vertical-agent","speech-lm","instruction-following"],"metrics":["task success","tool-selection accuracy","multi-turn stability","safety"],"models_scored":0,"updated":"2026-02","url":"https://github.com/ola-krutrim/VoiceAgentBench","paper_url":"https://arxiv.org/abs/2510.07978","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/voiceagentbench"} | |
| {"slug":"audio-multichallenge","name":"Audio MultiChallenge","maintainer":"Scale AI (Scale Labs)","tagline":"Multi-turn evaluation of spoken dialogue systems on natural human interaction — scores Inference Memory, Instruction Retention, Self Coherence, Voice Editing, Audio-Cue handling.","full_duplex":true,"tier":"native","setting":"lab","focus":["speech-lm","full-duplex"],"metrics":["Inference Memory","Instruction Retention","Self Coherence","Voice Editing","Audio-Cue"],"models_scored":2,"updated":"2026-10","url":"https://labs.scale.com/papers/audiomc","paper_url":"https://arxiv.org/abs/2512.14865","leaderboard_url":"https://labs.scale.com/leaderboard/audiomc","page_url":"https://www.fullduplex.ai/benchmarks/audio-multichallenge"} | |
| {"slug":"msi-bench","name":"MSI-Bench","maintainer":"Boson AI","tagline":"Multi-speaker voice-agent benchmark: short multi-party audio scenes with participant context, expected tool calls and atomic rubrics, in English and Mandarin.","full_duplex":false,"tier":"native","setting":"lab","focus":["speech-lm","vertical-agent"],"metrics":["all-rubric pass rate","multi-speaker memory","multi-speaker instruction following","multi-speaker reasoning"],"models_scored":0,"updated":"2026-09","url":"https://github.com/boson-ai/MSI-Bench","paper_url":"https://arxiv.org/abs/2609.24812","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/msi-bench"} | |
| {"slug":"mmedfd","name":"MMedFD","maintainer":"Academic (MMedFD authors)","tagline":"Real-world healthcare benchmark for multi-turn full-duplex automatic speech recognition — adjacent to STS, crucial for duplex deployment.","full_duplex":true,"tier":"adjacent","setting":"vertical","focus":["asr","vertical-agent","full-duplex"],"metrics":["duplex WER","medical-term recall","overlap-region WER"],"models_scored":0,"updated":"2025-09","url":"https://github.com/Kinetics-JOJO/MMedFD","paper_url":"https://arxiv.org/abs/2509.19817","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/mmedfd"} | |
| {"slug":"audio-jailbreaks-taxonomy","name":"LALM Jailbreak Taxonomy & Eval","maintainer":"Feng et al.","tagline":"Unified taxonomy of LALM jailbreak attacks (semantic, acoustic, signal, internal-representation) with controlled empirical evaluation under a single threat model and cost-aware protocol.","full_duplex":false,"tier":"native","setting":"lab","focus":["audio-safety","speech-lm"],"metrics":["attack success rate","cost-aware ASR","defense utility","cross-attack generalisation"],"models_scored":0,"updated":"2026-05","url":"https://arxiv.org/abs/2605.30031","paper_url":"https://arxiv.org/abs/2605.30031","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/audio-jailbreaks-taxonomy"} | |
| {"slug":"speechjbb","name":"SpeechJBB","maintainer":"Ceccatelli, Jeon, Adelani et al.","tagline":"Audio jailbreak benchmark for LALMs under multilingual and code-switched speech, with phonological perturbations as an augmented evaluation regime.","full_duplex":false,"tier":"native","setting":"lab","focus":["audio-safety","speech-lm"],"metrics":["code-switched ASR","phonological-perturbation success","multilingual alignment gap"],"models_scored":0,"updated":"2026-08","url":"https://arxiv.org/abs/2606.06037","paper_url":"https://arxiv.org/abs/2606.06037","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/speechjbb"} | |
| {"slug":"aia-acoustic-interference","name":"AIA — Acoustic Interference Attack","maintainer":"Wang, Huang, Liang, Wu (CUHK · HKUST)","tagline":"Universal-jailbreak benchmark for LALMs using Acoustic Latent Semantics — paralinguistic features intrinsic to the priors of audio generative models — that decouples attack payload from audio content.","full_duplex":false,"tier":"native","setting":"lab","focus":["audio-safety","speech-lm"],"metrics":["universal jailbreak success","alignment-evasion rate","transferability across LALMs"],"models_scored":0,"updated":"2026-05","url":"https://flaai.github.io/AIA_page/","paper_url":"https://arxiv.org/abs/2605.18168","leaderboard_url":null,"page_url":"https://www.fullduplex.ai/benchmarks/aia-acoustic-interference"} | |