Datasets:

Fullduplex-ai
/

catalog

slug stringlengths 4 25	name stringlengths 4 42	maintainer stringlengths 3 37	tagline stringlengths 69 200	full_duplex bool 2 classes	tier stringclasses 4 values	setting stringclasses 4 values	focus listlengths 1 4	metrics listlengths 1 5	models_scored int64 0 8	updated stringdate 2020-07-01 00:00:00 2026-11-01 00:00:00	url stringlengths 23 80	paper_url stringlengths 32 32 ⌀	leaderboard_url stringlengths 24 73 ⌀	page_url stringlengths 41 62
turnbench	TurnBench	Sesame AI Labs / Mundo AI / CMU / oto	Scores end-of-turn and interruption detection on 30 hours of hand-labelled dyadic conversation, with conversation type as a controlled variable.	true	native	lab	[ "full-duplex" ]	[ "end-of-turn recall", "interruption recall", "false-positive rate", "median detection latency" ]	3	2026-11	https://turnbench.sesame.com/	https://arxiv.org/abs/2608.25218	https://turnbench.sesame.com/	https://www.fullduplex.ai/benchmarks/turnbench
fdb-v1	Full-Duplex-Bench v1	NTU / UC Berkeley / UW / MIT	Original Full-Duplex-Bench — automatic scoring of the four canonical turn-taking events: when-to-speak, back-channel, interruption success, pause handling.	true	native	lab	[ "full-duplex" ]	[ "turn-taking latency", "back-channel timing", "interruption success", "pause handling" ]	5	2025-03	https://github.com/DanielLin94144/Full-Duplex-Bench	https://arxiv.org/abs/2503.04721	null	https://www.fullduplex.ai/benchmarks/fdb-v1
fdb-v15	Full-Duplex-Bench v1.5	NTU / UC Berkeley / UW / MIT	Mid-2025 refresh of FDB v1 — adds the simultaneous-speech / overlap axis (user interruption, listener back-channel, side conversation, ambient speech).	true	native	lab	[ "full-duplex" ]	[ "overlap duration", "mutual-silence rate", "interruption recovery" ]	3	2025-08	https://github.com/DanielLin94144/Full-Duplex-Bench	https://arxiv.org/abs/2507.23159	null	https://www.fullduplex.ai/benchmarks/fdb-v15
fdb-v2	Full-Duplex-Bench v2	NTU / UC Berkeley / UW / MIT	Late-2025 WebRTC-based live harness — first FDB release that runs dynamic evaluation against streaming endpoints, plus multi-turn stimuli.	true	native	live	[ "full-duplex", "speech-lm" ]	[ "streaming latency", "multi-turn coherence", "overlap handling" ]	0	2026-02	https://github.com/DanielLin94144/Full-Duplex-Bench	https://arxiv.org/abs/2510.07838	null	https://www.fullduplex.ai/benchmarks/fdb-v2
fdb-v3	Full-Duplex-Bench v3	NTU (NVIDIA advisory)	Multi-step tool calling for voice agents, driven by real human speech with natural disfluencies.	true	native	live	[ "full-duplex", "vertical-agent" ]	[ "Pass@1", "tool-call F1", "response latency", "turn-take rate" ]	2	2026-05	https://github.com/DanielLin94144/Full-Duplex-Bench	https://arxiv.org/abs/2604.04847	null	https://www.fullduplex.ai/benchmarks/fdb-v3
videofdb	VideoFDB	NVIDIA / David AI	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?	true	native	live	[ "full-duplex" ]	[ "perception rubric (0 to 5)", "generation rubric (0 to 5)", "TOR-Alignment", "median response latency" ]	4	2026-10	https://research.nvidia.com/labs/amri/projects/video-fdb/	https://arxiv.org/abs/2605.30256	https://research.nvidia.com/labs/amri/projects/video-fdb/leaderboard.html	https://www.fullduplex.ai/benchmarks/videofdb
duplexact	DuplexAct	Dong et al.	Scores whether full-duplex speech models take the right interaction action when a conversation calls for it, and whether they follow through.	true	native	lab	[ "full-duplex" ]	[ "immediate execution success", "follow-through success" ]	8	2026-10	https://github.com/JiahengDong/DuplexAct	https://arxiv.org/abs/2610.17738	null	https://www.fullduplex.ai/benchmarks/duplexact
multipartyturnbench	MultiPartyTurnBench	Mathur and Manocha	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?	true	native	lab	[ "full-duplex" ]	[ "end-of-turn recall at fixed FPR", "interruption recall at fixed FPR", "signed latency", "selective interruption balanced accuracy" ]	2	2026-10	https://multipartyturnbench.github.io/	https://arxiv.org/abs/2610.20172	null	https://www.fullduplex.ai/benchmarks/multipartyturnbench
multitalkbench	MultiTalkBench	Wang et al. (MultiTalk)	Long, multi-party, English and Chinese full-duplex dialogue, with conversations that average 32.6 minutes.	true	native	lab	[ "full-duplex" ]	[ "long-range entity tracking", "topic coherence", "addressee selection", "turn-taking" ]	0	2026-09	https://huggingface.co/datasets/MultiTalk/MultiTalkBench	https://arxiv.org/abs/2609.36903	null	https://www.fullduplex.ai/benchmarks/multitalkbench
m3-duplexbench	M3-DuplexBench	NTT (Fukuda et al.)	English and Japanese full-duplex benchmark that scores interaction timing and response content together, in casual chat and multi-turn QA.	true	native	lab	[ "full-duplex", "speech-lm" ]	[ "turn-taking rate and latency", "pause handling", "backchannel and barge-in response", "response content" ]	0	2026-10	https://github.com/nttcslab-sp/m3-duplexbench	https://arxiv.org/abs/2607.29125	null	https://www.fullduplex.ai/benchmarks/m3-duplexbench
duplex-mpe	Duplex-MPE	Ma et al.	Multi-party full-duplex test: with three or four people talking, should the assistant answer, stay silent or stop?	true	native	lab	[ "full-duplex" ]	[ "response initiation", "answer accuracy", "silence preservation", "stopping when resolved" ]	0	2026-11	https://step-out.github.io/Duplex-MPE-Page/	https://arxiv.org/abs/2609.31948	null	https://www.fullduplex.ai/benchmarks/duplex-mpe
duplexledger	DuplexLedger	Li et al.	Does a full-duplex agent remember the right things? Write, update, ignore and retain decisions during live interaction.	true	native	lab	[ "full-duplex", "speech-lm" ]	[ "latest-value accuracy", "final-state fact coverage", "stale assertions", "distractor adoption" ]	0	2026-10	https://github.com/lyx-researcher/DuplexLedger	https://arxiv.org/abs/2610.27581	null	https://www.fullduplex.ai/benchmarks/duplexledger
interactionbench	InteractionBench	Avera Labs (He et al.)	Timing, spoken-task accuracy, whisper and volume, and groundedness over 20+ turns, measured on the same recorded user audio for every system.	true	native	live	[ "full-duplex", "speech-lm" ]	[ "conversational timing", "spoken-task accuracy", "whisper and volume control", "groundedness" ]	0	2026-11	https://avera-labs.github.io/InteractionBench/	https://arxiv.org/abs/2611.02430	null	https://www.fullduplex.ai/benchmarks/interactionbench
echo-turn-taking	ECHO	Zhao et al.	Paired Chinese turn-taking test: the same overlapping words must sometimes take the floor and sometimes not, depending on context.	true	native	lab	[ "full-duplex" ]	[ "pair accuracy", "yield accuracy", "keep accuracy", "off-talk false yields" ]	0	2026-09	https://huggingface.co/datasets/shuofeng123/ECHO	https://arxiv.org/abs/2609.17360	null	https://www.fullduplex.ai/benchmarks/echo-turn-taking
mp-bench	MP-Bench	Shih et al.	The voice agent is one participant in a spoken group conversation: does it follow who said what, and does it take its turn?	true	native	lab	[ "full-duplex", "speech-lm" ]	[ "turn-taking accuracy", "response appropriateness", "comprehension accuracy", "latency" ]	0	2026-10	https://github.com/atosystem/MP-Bench	https://arxiv.org/abs/2609.13076	null	https://www.fullduplex.ai/benchmarks/mp-bench
dsb-ifeval	DuplexSpeechBench-IFEval	Mathur and Manocha	Does a full-duplex agent infer from its persona when to speak, backchannel, interrupt or yield, and not only from explicit rules?	true	native	lab	[ "full-duplex", "instruction-following" ]	[ "Instruction Adherence Score", "Persona Adherence Score" ]	0	2026-09	https://dsb-ifeval.github.io/	https://arxiv.org/abs/2609.03423	null	https://www.fullduplex.ai/benchmarks/dsb-ifeval
talking-turns	Talking Turns	Apple / CMU	A supervised turn-taking judge trained on human-human conversations to score audio foundation models.	true	native	lab	[ "full-duplex", "speech-lm" ]	[ "turn-event F1", "backchannel rate", "interruption rate" ]	3	2025-04	https://machinelearning.apple.com/research/talking-turns	https://arxiv.org/abs/2503.01174	null	https://www.fullduplex.ai/benchmarks/talking-turns
voicebench	VoiceBench	NUS	Evaluates LLM-based voice assistants across knowledge, reasoning, safety, and instruction-following — judged by a GPT-4-class model.	false	native	lab	[ "speech-lm", "instruction-following" ]	[ "overall", "QA", "reasoning (BBH)", "IFEval", "safety" ]	7	2025-06	https://github.com/MatthewCYM/VoiceBench	https://arxiv.org/abs/2410.17196	https://matthewcym.github.io/VoiceBench/	https://www.fullduplex.ai/benchmarks/voicebench
vocalbench	VocalBench	SJTU (OmniAgent)	A four-dimensional stress test for speech-interaction models — semantic, acoustic, conversational, and robust.	false	native	lab	[ "speech-lm", "audio-understanding" ]	[ "ACC", "LLM-judge", "UTMOS", "WER", "EER" ]	0	2026-01	https://github.com/SJTU-OmniAgent/VocalBench	https://arxiv.org/abs/2505.15727	null	https://www.fullduplex.ai/benchmarks/vocalbench
uro-bench	URO-Bench	Ruiqi Yan et al.	The first end-to-end S2S benchmark with multilingual, multi-turn, and paralinguistic coverage.	false	native	lab	[ "speech-lm", "instruction-following", "emotion" ]	[ "U/R/O composite", "paralinguistic score" ]	2	2025-08	https://github.com/Ruiqi-Yan/URO-Bench	https://arxiv.org/abs/2502.17810	null	https://www.fullduplex.ai/benchmarks/uro-bench
air-bench	AIR-Bench	Alibaba DAMO (OFA-Sys)	A two-tier stress test for audio-LLMs covering speech, natural sound, and music — MCQ plus chat.	false	native	lab	[ "audio-understanding", "instruction-following" ]	[ "MCQ acc", "GPT-4 judge score" ]	3	2024-12	https://github.com/OFA-Sys/AIR-Bench	https://arxiv.org/abs/2402.07729	null	https://www.fullduplex.ai/benchmarks/air-bench
big-bench-audio	Big Bench Audio	Artificial Analysis	1 000 audio-question reasoning benchmark — the dataset behind the Artificial Analysis S2S speech-reasoning leaderboard.	false	native	lab	[ "speech-lm", "instruction-following" ]	[ "accuracy", "TTFA (s)" ]	7	2026-05	https://huggingface.co/datasets/ArtificialAnalysis/big_bench_audio	null	https://artificialanalysis.ai/speech-to-speech	https://www.fullduplex.ai/benchmarks/big-bench-audio
aa-speech-to-speech-index	Artificial Analysis Speech to Speech Index	Artificial Analysis	Composite score for native speech-to-speech models: speech reasoning, agentic task completion, human preference and task success, weighted equally.	false	native	lab	[ "speech-lm", "vertical-agent" ]	[ "index score", "Big Bench Audio accuracy", "τ-Voice pass rate", "Arena Score", "task success rate" ]	0	2026-08	https://artificialanalysis.ai/methodology/speech-to-speech-benchmarking	null	https://artificialanalysis.ai/speech-to-speech	https://www.fullduplex.ai/benchmarks/aa-speech-to-speech-index
mmar	MMAR	SJTU / MBZUAI · NeurIPS 2025	1 000 human-curated deep-reasoning audio QA items spanning speech, sound, music, and their mix — four hierarchical reasoning layers.	false	component	lab	[ "audio-understanding", "speech-lm" ]	[ "overall accuracy", "per-layer accuracy", "rubric score" ]	2	2026-02	https://github.com/ddlBoJack/MMAR	https://arxiv.org/abs/2505.13032	null	https://www.fullduplex.ai/benchmarks/mmar
ifeval-audio	IFEval-Audio	AudioLLMs (I2R Singapore)	Format-constrained spoken instructions, verified programmatically — an audio port of text IFEval.	false	native	lab	[ "instruction-following", "speech-lm" ]	[ "IFR", "SCR", "OSR" ]	0	2025-05	https://github.com/AudioLLMs/AudioBench/tree/main/IFEval-Audio	https://arxiv.org/abs/2505.16774	null	https://www.fullduplex.ai/benchmarks/ifeval-audio
scale-voice-showdown	Scale Voice Showdown	Scale AI	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.	false	native	arena	[ "speech-lm", "tts" ]	[ "preference win-rate", "per-language score" ]	1	2026-03	https://labs.scale.com/	null	null	https://www.fullduplex.ai/benchmarks/scale-voice-showdown
aa-speech-arena	Artificial Analysis Speech Arena	Artificial Analysis	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.	false	adjacent	arena	[ "tts" ]	[ "Elo" ]	0	2026-10	https://artificialanalysis.ai/text-to-speech/arena	null	https://artificialanalysis.ai/text-to-speech/leaderboard	https://www.fullduplex.ai/benchmarks/aa-speech-arena
aa-speech-agent-arena	Artificial Analysis Speech Agent Arena	Artificial Analysis	Human participants complete the same task with two hidden speech-to-speech systems over live calls, then vote for the conversation they preferred.	false	native	arena	[ "speech-lm", "vertical-agent" ]	[ "preference Elo", "agentic Elo", "non-agentic Elo", "task success rate" ]	0	2026-10	https://artificialanalysis.ai/speech-to-speech/arena/overview	null	https://artificialanalysis.ai/speech-to-speech/arena	https://www.fullduplex.ai/benchmarks/aa-speech-agent-arena
rw-voice-eq	Real World Voice EQ Bench	Hume AI	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.	false	component	arena	[ "tts", "speech-lm", "asr", "emotion" ]	[ "rater score (1 to 5) per factor", "WER (ASR board)" ]	0	2026-07	https://huggingface.co/spaces/HumeAI/rw-voice-eq	https://arxiv.org/abs/2607.14846	https://huggingface.co/spaces/HumeAI/rw-voice-eq	https://www.fullduplex.ai/benchmarks/rw-voice-eq
mtalk-bench	MTalk-Bench	FreedomIntelligence / CUHK-SZ	First multi-turn S2S benchmark with both arena-style (pairwise) and rubric-based (absolute) protocols for dialogue evaluation.	false	native	arena	[ "speech-lm", "instruction-following", "emotion" ]	[ "arena win-rate", "rubric score", "per-dimension score" ]	0	2025-09	https://github.com/FreedomIntelligence/MTalk-Bench	https://arxiv.org/abs/2508.18240	null	https://www.fullduplex.ai/benchmarks/mtalk-bench
tts-arena	TTS Arena	Hugging Face	The original blind A/B voting space for TTS, feeding an Elo ladder on Hugging Face.	false	adjacent	arena	[ "tts" ]	[ "Elo" ]	0	2026-03	https://huggingface.co/spaces/TTS-AGI/TTS-Arena-V2	null	https://huggingface.co/spaces/TTS-AGI/TTS-Arena-V2	https://www.fullduplex.ai/benchmarks/tts-arena
tau3-bench	τ-bench 2.0 (τ-Voice)	Sierra Research	The first agent benchmark with a live, full-duplex voice track, now in six languages with version 2.0.	true	native	vertical	[ "vertical-agent", "full-duplex", "speech-lm" ]	[ "pass^k", "tool-use success", "voice task success", "per-language task completion" ]	3	2026-10	https://sierra.ai/blog/bench-advancing-agent-benchmarking-to-knowledge-and-voice	https://arxiv.org/abs/2603.13686	https://www.taubench.com	https://www.fullduplex.ai/benchmarks/tau3-bench
eva-bench	EVA-Bench	ServiceNow	End-to-end voice-agent evaluation with bot-to-bot calls, separate accuracy and experience scores, and accent and noise perturbations.	false	native	vertical	[ "vertical-agent", "speech-lm" ]	[ "EVA-A (accuracy)", "EVA-X (experience)", "pass@k and pass^k", "accent and noise robustness" ]	1	2026-10	https://servicenow.github.io/eva/	https://arxiv.org/abs/2605.13841	https://servicenow.github.io/eva/	https://www.fullduplex.ai/benchmarks/eva-bench
vamos-bench	VAmoS Bench	Veris AI	Simulated phone calls graded on what the voice agent said and on what it actually changed in the backend.	false	native	vertical	[ "vertical-agent" ]	[ "task completion", "latency", "barge-ins per call", "cost per call" ]	0	2026-10	https://veris.ai/leaderboard/vamos	https://arxiv.org/abs/2609.38512	https://veris.ai/leaderboard/vamos-pro	https://www.fullduplex.ai/benchmarks/vamos-bench
apex-voice	APEX-Voice	Mathur and Manocha	Can a full-duplex voice agent finish professional work, such as form completion or negotiation, while the user interrupts and corrects?	true	native	vertical	[ "vertical-agent", "full-duplex" ]	[ "Pass@1", "Reliable@3", "artifact field accuracy" ]	0	2026-09	https://apex-voice.github.io/	https://arxiv.org/abs/2609.34973	null	https://www.fullduplex.ai/benchmarks/apex-voice
voiceagenteval	VoiceAgentEval	Meituan / Xbench / Agora	A dual-dimensional benchmark for expert-level outbound-calling agents — interaction fluency vs task flow compliance.	false	native	vertical	[ "vertical-agent", "speech-lm" ]	[ "GIC", "TFC", "task success" ]	0	2025-10	https://www.shengwang.cn/news/blogdetail/VoiceAgentEval/	https://arxiv.org/abs/2510.21244	null	https://www.fullduplex.ai/benchmarks/voiceagenteval
healthbench	HealthBench	OpenAI	5,000 multi-turn health conversations × 48k physician-written rubric criteria — text-only, listed as a transferable foundation rubric rather than a voice benchmark.	false	adjacent	vertical	[ "vertical-agent", "instruction-following" ]	[ "rubric score", "per-theme breakdown" ]	0	2025-05	https://huggingface.co/datasets/openai/healthbench	https://arxiv.org/abs/2505.08775	null	https://www.fullduplex.ai/benchmarks/healthbench
seamless-expressive	SeamlessExpressive (mExpresso)	Meta AI	Expressive speech-to-speech translation judged on translation, prosody, and vocal-style preservation.	false	component	lab	[ "s2st" ]	[ "ASR-BLEU", "AutoPCP", "Vocal Style Sim" ]	0	2023-12	https://github.com/facebookresearch/seamless_communication	https://arxiv.org/abs/2312.05187	null	https://www.fullduplex.ai/benchmarks/seamless-expressive
covost2	CoVoST-2 BLEU	Meta AI	21 language pairs of speech-to-text translation, long the S2T standard.	false	component	lab	[ "s2st", "asr" ]	[ "BLEU", "chrF" ]	0	2020-07	https://github.com/facebookresearch/covost	https://arxiv.org/abs/2007.10310	null	https://www.fullduplex.ai/benchmarks/covost2
seed-tts-eval	Seed-TTS Eval	ByteDance	The de-facto evaluation set for zero-shot voice cloning — WER, speaker similarity, and CMOS in one recipe.	false	component	lab	[ "tts" ]	[ "WER", "SECS", "CMOS" ]	0	2024-11	https://bytedancespeech.github.io/seedtts_tech_report/	https://arxiv.org/abs/2406.02430	null	https://www.fullduplex.ai/benchmarks/seed-tts-eval
nv-bench	NV-Bench	Ni et al.	Standardised test for generating laughs, sighs, fillers, and back-channels in TTS.	false	component	lab	[ "tts", "emotion" ]	[ "PCER", "FAD", "SIM", "DNSMOS" ]	0	2026-03	https://arxiv.org/abs/2603.15352	https://arxiv.org/abs/2603.15352	null	https://www.fullduplex.ai/benchmarks/nv-bench
superb	SUPERB	NTU et al. (s3prl)	The classic: general-purpose speech representations scored on ten frozen-encoder downstream tasks.	false	peripheral	lab	[ "speech-lm", "asr", "audio-understanding" ]	[ "composite score", "per-task scores" ]	0	2024-06	https://github.com/s3prl/s3prl	https://arxiv.org/abs/2105.01051	null	https://www.fullduplex.ai/benchmarks/superb
librispeech	LibriSpeech WER	OpenSLR	The coordinate system of English ASR — test-clean and test-other WER.	false	peripheral	lab	[ "asr" ]	[ "WER (test-clean)", "WER (test-other)" ]	0	2024-01	https://www.openslr.org/12	null	null	https://www.fullduplex.ai/benchmarks/librispeech
meld	MELD / IEMOCAP	Academic	The classic emotion-recognition pair, evaluated with full dialogue context.	false	peripheral	lab	[ "emotion", "audio-understanding" ]	[ "Weighted F1", "Accuracy" ]	0	2023-06	https://affective-meld.github.io	null	null	https://www.fullduplex.ai/benchmarks/meld
sid-bench	SID-Bench	Xia et al.	Semantic-aware interruption detection, built from real human dialogues and scored with a time penalty for both false alarms and late responses.	true	native	lab	[ "full-duplex" ]	[ "Average Penalty Time (APT)", "false alarms", "response delay" ]	0	2026-03	https://github.com/xkx-hub/SID-bench	https://arxiv.org/abs/2603.24144	null	https://www.fullduplex.ai/benchmarks/sid-bench
fd-bench	FD-Bench	Peng et al.	A pipeline of LLM, TTS and ASR that simulates users and tests how full-duplex systems handle interruptions, delays and noise.	true	native	lab	[ "full-duplex" ]	[ "interruption response", "delay", "robustness under noise" ]	0	2025-07	https://github.com/pengyizhou/FD-Bench	https://arxiv.org/abs/2507.19040	null	https://www.fullduplex.ai/benchmarks/fd-bench
mtr-duplex-bench	MTR-DuplexBench	Zhang et al.	Multi-round benchmark that splits continuous full-duplex dialogue into turns and scores each round.	true	native	lab	[ "full-duplex", "speech-lm" ]	[ "conversational features", "dialogue quality", "instruction following", "safety" ]	0	2026-04	https://github.com/ZhangHe0918/MTR-DuplexBench	https://arxiv.org/abs/2511.10262	null	https://www.fullduplex.ai/benchmarks/mtr-duplex-bench
humdial	HumDial (ICASSP '26)	ICASSP 2026 Spoken Dialogue Challenge	Dual-track human-evaluated spoken dialogue challenge — first public benchmark to explicitly score paralinguistic input alongside turn-taking dynamics.	true	native	lab	[ "full-duplex", "speech-lm", "emotion" ]	[ "turn-taking", "paralinguistic grounding", "human preference" ]	0	2026-01	https://aslp-lab.github.io/HumDial-Challenge/	https://arxiv.org/abs/2601.05564	null	https://www.fullduplex.ai/benchmarks/humdial
flexi	FLEXI	Ge et al.	Full-duplex benchmark that includes the model interrupting the user in emergencies, across six interaction scenarios.	true	native	lab	[ "full-duplex" ]	[ "latency", "response quality", "emergency interruption" ]	0	2025-09	https://github.com/ChristineCHEN274/FLEXI	https://arxiv.org/abs/2509.22243	null	https://www.fullduplex.ai/benchmarks/flexi
audiobench	AudioBench	AudioLLMs (I2R Singapore)	Broad audio-understanding benchmark covering speech, music, and ambient sound reasoning.	false	component	lab	[ "audio-understanding" ]	[ "per-track accuracy", "overall" ]	0	2024-07	https://github.com/AudioLLMs/AudioBench	https://arxiv.org/abs/2406.16020	null	https://www.fullduplex.ai/benchmarks/audiobench
dynamic-superb	Dynamic-SUPERB	NTU et al.	Instruction-tuned successor to SUPERB, with 180 tasks in Phase-2 across speech understanding and light paralinguistic input.	false	component	lab	[ "speech-lm", "instruction-following" ]	[ "per-task accuracy", "composite score" ]	0	2024-11	https://github.com/dynamic-superb/dynamic-superb	https://arxiv.org/abs/2411.05361	null	https://www.fullduplex.ai/benchmarks/dynamic-superb
voiceassistant-eval	VoiceAssistant-Eval	Wang et al.	10,497 examples in 13 task categories that test voice assistants on listening, speaking and viewing.	false	component	lab	[ "speech-lm", "instruction-following" ]	[ "content quality", "speech quality", "consistency" ]	0	2025-09	https://mathllm.github.io/VoiceAssistantEval/	https://arxiv.org/abs/2509.22651	null	https://www.fullduplex.ai/benchmarks/voiceassistant-eval
sd-eval	SD-Eval	Academic (SD-Eval authors)	Baseline probe on paralinguistic input — does a speech-LM use tone, emotion, and speaker traits at all?	false	component	lab	[ "speech-lm", "emotion" ]	[ "paralinguistic-grounded accuracy", "delta over ASR baseline" ]	0	2024-06	https://arxiv.org/abs/2406.13340	https://arxiv.org/abs/2406.13340	null	https://www.fullduplex.ai/benchmarks/sd-eval
prosaudit	ProsAudit	Academic (ProsAudit authors)	Prosodic boundary detection benchmark — can the model perceive phrase / sentence boundaries from audio alone?	false	component	lab	[ "emotion", "audio-understanding" ]	[ "boundary F1", "delta over text-only" ]	0	2023-02	https://arxiv.org/abs/2302.12057	https://arxiv.org/abs/2302.12057	null	https://www.fullduplex.ai/benchmarks/prosaudit
vocalbench-zh	VocalBench-zh	SJTU (OmniAgent)	Mandarin version of VocalBench — 10 subsets, ~10k instances, 14 models evaluated at the Nov 2025 launch.	false	native	lab	[ "speech-lm", "audio-understanding" ]	[ "ACC", "LLM-judge", "UTMOS", "WER", "EER" ]	0	2025-11	https://github.com/SJTU-OmniAgent/VocalBench-zh	https://arxiv.org/abs/2511.08230	null	https://www.fullduplex.ai/benchmarks/vocalbench-zh
cs3-bench	CS3-Bench	Academic (CS3-Bench authors)	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.	false	native	lab	[ "speech-lm" ]	[ "monolingual-vs-codeswitch delta", "per-pair accuracy" ]	0	2025-10	https://huggingface.co/datasets/VocalNet/CS3-Bench	https://arxiv.org/abs/2510.07881	null	https://www.fullduplex.ai/benchmarks/cs3-bench
j-moshi	J-Moshi (subjective)	Ohashi et al.	The first public Japanese full-duplex dialogue model, built on Moshi and rated by human listeners for naturalness and meaningfulness.	true	native	lab	[ "full-duplex", "speech-lm" ]	[ "naturalness", "meaningfulness" ]	0	2025-06	https://nu-dialogue.github.io/j-moshi	https://arxiv.org/abs/2506.02979	null	https://www.fullduplex.ai/benchmarks/j-moshi
voiceagentbench	VoiceAgentBench	Academic (VoiceAgentBench authors)	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.	false	native	vertical	[ "vertical-agent", "speech-lm", "instruction-following" ]	[ "task success", "tool-selection accuracy", "multi-turn stability", "safety" ]	0	2026-02	https://github.com/ola-krutrim/VoiceAgentBench	https://arxiv.org/abs/2510.07978	null	https://www.fullduplex.ai/benchmarks/voiceagentbench
audio-multichallenge	Audio MultiChallenge	Scale AI (Scale Labs)	Multi-turn evaluation of spoken dialogue systems on natural human interaction — scores Inference Memory, Instruction Retention, Self Coherence, Voice Editing, Audio-Cue handling.	true	native	lab	[ "speech-lm", "full-duplex" ]	[ "Inference Memory", "Instruction Retention", "Self Coherence", "Voice Editing", "Audio-Cue" ]	2	2026-10	https://labs.scale.com/papers/audiomc	https://arxiv.org/abs/2512.14865	https://labs.scale.com/leaderboard/audiomc	https://www.fullduplex.ai/benchmarks/audio-multichallenge
msi-bench	MSI-Bench	Boson AI	Multi-speaker voice-agent benchmark: short multi-party audio scenes with participant context, expected tool calls and atomic rubrics, in English and Mandarin.	false	native	lab	[ "speech-lm", "vertical-agent" ]	[ "all-rubric pass rate", "multi-speaker memory", "multi-speaker instruction following", "multi-speaker reasoning" ]	0	2026-09	https://github.com/boson-ai/MSI-Bench	https://arxiv.org/abs/2609.24812	null	https://www.fullduplex.ai/benchmarks/msi-bench
mmedfd	MMedFD	Academic (MMedFD authors)	Real-world healthcare benchmark for multi-turn full-duplex automatic speech recognition — adjacent to STS, crucial for duplex deployment.	true	adjacent	vertical	[ "asr", "vertical-agent", "full-duplex" ]	[ "duplex WER", "medical-term recall", "overlap-region WER" ]	0	2025-09	https://github.com/Kinetics-JOJO/MMedFD	https://arxiv.org/abs/2509.19817	null	https://www.fullduplex.ai/benchmarks/mmedfd
audio-jailbreaks-taxonomy	LALM Jailbreak Taxonomy & Eval	Feng et al.	Unified taxonomy of LALM jailbreak attacks (semantic, acoustic, signal, internal-representation) with controlled empirical evaluation under a single threat model and cost-aware protocol.	false	native	lab	[ "audio-safety", "speech-lm" ]	[ "attack success rate", "cost-aware ASR", "defense utility", "cross-attack generalisation" ]	0	2026-05	https://arxiv.org/abs/2605.30031	https://arxiv.org/abs/2605.30031	null	https://www.fullduplex.ai/benchmarks/audio-jailbreaks-taxonomy
speechjbb	SpeechJBB	Ceccatelli, Jeon, Adelani et al.	Audio jailbreak benchmark for LALMs under multilingual and code-switched speech, with phonological perturbations as an augmented evaluation regime.	false	native	lab	[ "audio-safety", "speech-lm" ]	[ "code-switched ASR", "phonological-perturbation success", "multilingual alignment gap" ]	0	2026-08	https://arxiv.org/abs/2606.06037	https://arxiv.org/abs/2606.06037	null	https://www.fullduplex.ai/benchmarks/speechjbb
aia-acoustic-interference	AIA — Acoustic Interference Attack	Wang, Huang, Liang, Wu (CUHK · HKUST)	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.	false	native	lab	[ "audio-safety", "speech-lm" ]	[ "universal jailbreak success", "alignment-evasion rate", "transferability across LALMs" ]	0	2026-05	https://flaai.github.io/AIA_page/	https://arxiv.org/abs/2605.18168	null	https://www.fullduplex.ai/benchmarks/aia-acoustic-interference

Fullduplex catalogue

The models, datasets and benchmarks for full-duplex and speech-to-speech voice AI that fullduplex.ai tracks, one row per entry. Every row links to the entry's own pages (official page, paper, repository) and to its page on fullduplex.ai.

Config	Rows	One row per
`models`	70	model, hosted API, voice-agent platform or framework
`datasets`	107	speech dataset
`benchmarks`	64	benchmark

Snapshot of 2026-11-09. The site is updated every week, so it can be newer than this file.

Fields worth knowing

full_duplex: for models, one model listens and speaks at the same time (duplex_type is full-duplex; the others are cascade, half-duplex and other). For datasets, each speaker is recorded on a separate channel. For benchmarks, the benchmark measures full-duplex behaviour such as turn-taking and interruptions.
first_response_ms: the vendor's own claim, not a measurement by fullduplex.ai.
speaker_consent: consented, public (collected from public media), mixed or unknown.
license_class: permissive, share-alike, non-commercial, gated or custom. The exact terms are in license.
page_url: the entry's page on fullduplex.ai, with the sources and the weekly reports that mention it.

Load it

from datasets import load_dataset

models = load_dataset("Fullduplex-ai/catalog", "models", split="train")

Licence and citation

CC BY 4.0. Please credit Fullduplex and link to https://www.fullduplex.ai.

@misc{fullduplex_catalog,
  title        = {Fullduplex catalogue: models, datasets and benchmarks for full-duplex voice AI},
  author       = {{Fullduplex}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Fullduplex-ai/catalog}}
}

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