Search: “realtime-speech”

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Google Releases Gemini 3.8 Live and 3.8 Live Extended Thinking for Production Grade Voice Agents

Google launches Gemini 3.8 Live and Extended Thinking speech-to-speech models for production voice agents, topping speech-to-speech benchmarks.

Google released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, native speech-to-speech models for real-time voice agents, available hosted via the Gemini Live API and AI Studio. Extended Thinking ranks #1 on Artificial Analysis' Speech-to-Speech Quality Index with 82.6, scores 68.6% on τ-Voice, 35.1% on Sierra's τ-Voice-banking, and 97.7% on Big Bench Audio. The models support asynchronous function calling, near-real-time visual context, alphanumeric precision, and 97 languages, priced at $0.005/min audio input and $0.018/min audio output. All generated audio carries Google DeepMind's imperceptible SynthID watermark.

MarkTechPost·Sep 15, 20:53 UTC · 1d agoModel release 3 sources

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StepAudio 3 Realtime Technical Report

StepAudio 3 Realtime debuts an audio-language model with Think-While-Speaking reasoning, delivering full-duplex voice dialogue with top benchmark results.

StepAudio 3 Realtime is an audio-language foundation model built around a continuous listen-converse-think-act loop for real-time spoken interaction. Think-While-Speaking runs private reasoning in parallel with speech, reaching a 73.0 macro average on StepAudioChat in reasoning mode. The model reports 90.6 on MMSU, 98.9 overall on the Artificial Analysis Full-Duplex Bench, and 56.0% macro task success on tau-Voice. An integrated Voice Agent handles asynchronous tool execution without disrupting dialogue flow.

Hugging Face daily papers·Sep 11, 20:00 UTC · 5d agoAI research

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

Realtime-Venus introduces two 9B full-duplex interaction models (Omni and Audio) that outperform Gemini 3.1 Live and GPT-4o on continuation metrics.

Realtime-Venus is a proactive full-duplex interaction system built on two separately trained 9B models: Realtime-Venus-Omni for audio-visual interaction and Realtime-Venus-Audio for spoken interaction. A dual-loop runtime lets foreground interaction continue while Realtime-Venus-Harness asynchronously executes background reasoning and tool tasks. Realtime-Venus-Omni leads on six of eight video benchmarks, including StreamingBench (70.2%), OVO-Bench (64.7%), and Daily-Omni (81.3%), while Realtime-Venus-Audio tops MMAU (78.0%) and MMAU-Pro (63.2%). On Full-Duplex-Bench v1.5, Realtime-Venus-Audio handles 75% of interruptions and exceeds Gemini 3.1 Live and GPT-4o on all three continuation metrics.

Hugging Face daily papers·Sep 11, 20:00 UTC · 5d agoAI research

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Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

Researchers distill a compact 82M-parameter Thai TTS from synthetic OmniVoice data, enabling on-device fixed-voice synthesis without reference audio.

The paper uses a large voice-cloning model (OmniVoice) as a synthetic data source to train Wayu-Paxa-TTS-Edge, an 82M-parameter fixed-voice Thai TTS student. The model achieves 68.2% Challenge-Set Keyword Accuracy (85.5% of Gemini 3.1), 91.4% pause precision, and CERs of 3.7% on Thai and 1.1% on English. It outperforms its teacher on pause placement and is open-sourced with its evaluation framework.

Hugging Face daily papers·Sep 2, 20:00 UTC · 14d agoAI research

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OpenAI's GPT-Live-1 API lets developers build apps that talk and listen at the same time

OpenAI's GPT-Live-1 API enables full-duplex speech apps, outperforming GPT-Realtime-2.1 with 0.8s turn-taking latency at $0.05 per minute.

OpenAI released GPT-Live-1 as a developer API for full-duplex speech, allowing applications to listen and talk simultaneously; it already powers ChatGPT voice. Benchmarks show 80.1% on full-duplex interactivity versus 45.4% for GPT-Realtime-2.1, turn-taking latency of 0.8 seconds versus 1.4 seconds, tool-calling accuracy of 87% versus 60%, and a 32% pass rate on a banking voice-support benchmark versus 12.4%. The model costs $0.05 per minute, ships twelve new voices, provides ASR transcripts and response text, and is already in production at Yelp for phone-based reservations.

The Decoder·Sep 10, 17:47 UTC · 6d agoModel release1

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MP-Bench: Evaluating Voice Agents as a Multiparty Conversation Participant

MP-Bench is the first benchmark for voice agents in multiparty conversations, finding real-time agents near chance on turn-taking.

MP-Bench is the first benchmark designed to objectively evaluate conversational speech systems as active participants in multi-party conversations. It assesses agents on turn-taking awareness and response appropriateness, with comprehension-based question-answering as a complementary evaluation. Benchmarking 12 voice agents shows real-time agents score at or below 22% on multiparty comprehension and remain near chance on multiparty turn-taking.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:11 UTC · 5d agoAI research2

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What Did I Just Say? Self-Listening for Full-Duplex Speech Models

Researchers propose Self-Listening, a full-duplex speech approach feeding realized model speech back as input to improve interruption recovery.

Full-duplex spoken language models can listen and speak simultaneously, but asynchronous text generation, speech synthesis, and playback cause mismatches between what a model believes it said and what the user heard. The paper defines the resulting recovery problem as anchor interruption and proposes Self-Listening, which interleaves user speech, model text, and played speech as input streams. The authors also release AnchorSpeech, a benchmark with homogeneous training and test splits tracking which ordered response items were actually spoken. Experiments show self-listening models achieve better anchoring performance than full-duplex baselines.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

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Gemini Live audio

Google released Gemini 3.8 Live speech-to-speech models; Simon Willison built a library-free browser web UI for real-time voice conversations with them.

Google released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, two new speech-to-speech models comparable in shape to OpenAI's GPT-Live family. Simon Willison had GPT-6 Astra Extra High build him a web UI for testing the models, supporting model and voice preset selection, optional system prompts, and interrupting the model mid-speech. The implementation uses no libraries and talks directly to Google's wss://generativelanguage.googleapis.com BidiGenerateContent WebSocket endpoint.

Simon Willison·Sep 15, 22:47 UTC · 1d agoModel release

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Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS

NVIDIA's Magpie TTS open-weight multilingual speech model enables low-latency voice agents with full deployment control.

Hugging Face's blog highlights NVIDIA Magpie TTS, an open-weights multilingual text-to-speech model designed for building low-latency voice agents. The open licensing gives developers full deployment control, allowing self-hosted multilingual speech for agentic applications. The post walks through building voice agents with the model.

Hugging Face Blog·Aug 10, 16:25 UTC · Aug 10, 2026Model release

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Build more natural voice experiences with GPT‑Live‑1 in the API

OpenAI releases GPT-Live-1 in the API, a full-duplex voice model that handles interruptions natively and delegates reasoning to backend models.

OpenAI launched GPT-Live-1 in the API, a single-model full-duplex voice system that listens and speaks simultaneously, replacing chained STT-LLM-TTS architectures. It improves Full Duplex Bench performance by 30 percentage points over GPT-Realtime-2.1 and ranks #1 on Tau3 when paired with GPT-6 Astra at medium reasoning effort. Early partner Speak reported nearly 80% fewer interruptions in language tutoring. The API release costs $0.05 per minute for the front-end voice layer and supports telephony, native ASR transcripts, keyword biasing, and expanded voice and language options.

OpenAI News·Sep 10, 00:00 UTC · 7d agoModel release1

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Motion-Omni: End-to-End Joint Speech and Full-Body Motion for Spoken Dialogue

Researchers introduce Motion-Omni, an end-to-end model generating speech with synchronized full-body motion, responding 5.4x faster than cascade pipelines.

Motion-Omni is an end-to-end framework in which a spoken dialogue model outputs facial expressions and hand, upper-body, and lower-body motion directly from the hidden states that produce speech, replacing two-stage cascade pipelines. Trained on 422,856 quality-ranked pseudo-labeled pairs (1,402 hours) with a Qwen2.5-7B-Instruct backbone, Motion-Omni-Q7 matches its teacher cascade within 2% on reference-free motion metrics, achieves a 2.62% word error rate, and runs faster than real time (RTF=0.78). The authors also release the SwDA-500 dataset and the first public evaluation protocol for stochastic open-ended full-body spoken dialogue.

Hugging Face daily papers·Aug 27, 20:00 UTC · 20d agoAI research1

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AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoModel release2

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PACE: Perceived-Latency-Aware Cascading Service Routing and Filler Control for QoE-Efficient Retrieval-Augmented Dialogue Serving

PACE cuts perceived latency in retrieval-augmented dialogue serving via cascading routing and filler control, reaching 0.41s P95 under load.

PACE is a serving framework for retrieval-augmented dialogue that optimizes Perceived Time-to-First-Response (PTFR) under quality and cost constraints. It combines a load-adaptive cascading router, a joint path-filler controller, and volatility-aware cache admission, deployed on a humanoid-robot sales service. On 75k CarQA requests, the cascade halved pure-LLM P95 PTFR (0.29s vs 0.53s) and the adaptive controller reached 0.41s P95, 2.4x better than RAG at high load; filler calls dropped 94% and stale answers fell from 86% to 0%.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:02 UTC · 7d agoAI tools & infra

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Nuha-Speech: Building General-Purpose Arabic Speech-LLMs

Nuha-Speech initiative builds general-purpose Arabic speech-LLMs using a 1.5M-sample speech QA corpus and fine-tuned Qwen-Omni variants.

The paper introduces Nuha-Speech, an initiative covering dataset construction, model training, and evaluation for Arabic speech large language models. The authors built an Arabic Speech Question-Answering corpus of over 1.5 million training samples and used it for supervised fine-tuning of Qwen-Omni model variants at multiple scales. A tailored evaluation framework with diverse tasks and metrics is designed to assess Arabic speech capabilities under limited resource constraints.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:50 UTC · 6d agoAI research1

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Watch out: Apple timepiece can grab snippets of conversation without both speakers' consent

Apple's Watch Series 12 Live Rewind and Siri Recap transcribe nearby conversations without bystander consent, drawing EFF criticism over all-party-consent laws.

Apple Watch Series 12's Audio Intelligence features on the S11 chip include Live Rewind, which transcribes the last 15 seconds of a conversation after a Digital Crown double-press, processing audio in a Secure Exclave and routing it to a nearby iPhone. Siri Recap generates AI summaries of daily conversations without retaining raw audio or attributing speakers. Apple says an audible chime and visual cue alert bystanders, but privacy advocates including the EFF note that 11 US states require all-party consent for recording and that bystanders have no practical way to opt in or decline.

The Register · Security·Sep 10, 19:50 UTC · 6d agoAI safety & security

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Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation

Researchers present Puppeteer, a posture-aware, object-grounded diffusion model generating physically consistent co-speech gestures with temporal control.

Puppeteer decomposes long gestures into structured primitives encoded by a causal variational autoencoder into temporally ordered latent tokens. Conditional diffusion in the causal latent space conditions on speech signals, motion history, an initial posture reference, and object geometry to synthesize physically consistent gestures. The authors also introduce new evaluation metrics and release SceneGes, the first curated synthetic 3D dataset of embodied co-speech gestures with corresponding 3D objects.

Hugging Face daily papers·Aug 30, 20:00 UTC · 17d agoAI research1

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Building a Production Greek-English Speech Recognizer

Engineering report details Sophea, a production Greek-English ASR reaching 4.26% WER on public English sets via ROVER ensemble and data-pipeline calibration.

Across 23 training iterations, two architectures, and nine production gates, no single data composition passed all gates; a three-model ROVER ensemble reached 9 of 9 gates and cut overlapping-speech WER from 53.35% to 37.87%. Calibrating an audio-quality filter against in-domain anchors reduced discarded scored Greek audio from 98.7% to 10.6%, and a pre-registered ablation traced a hallucination defect to one training-data package. The sophea/asr-k1 preview arbiter lists 4.26% average WER on eight public English test sets and 25.88% WER on live Greek noisy traffic; no weights or training data are released.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research

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Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents

Duplex Cue evaluation shows PersonaPlex full-duplex agents adapt in-turn to listener contributions in only 34.8% of collaborative cases versus 68.2% for humans.

The paper introduces Duplex Cue, an evaluation of in-turn adaptation in full-duplex voice agents that separates listener intent (backchannel, collaboration, interruption) from speaker behavior (continue, adapt, yield). Using 208 scorable pairs from 300 human-confirmed cues in unscripted English conversations, it compares recorded human responses with PersonaPlex continuations generated while replaying listener audio. Humans adapt within the turn in 68.2% of collaborative pairs versus 34.8% for PersonaPlex, which otherwise continues unchanged (42.4%) or yields (22.7%).

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:46 UTC · 5d agoAI research

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

ECHO benchmark pairs identical-overlap Chinese dialogue examples with contrasting contexts, revealing most full-duplex systems bias toward yielding the floor.

ECHO is a paired diagnostic benchmark for Chinese full-duplex turn-taking that matches examples with identical overlap transcripts but contrasting preceding multi-turn contexts, one requiring Yield and the other Keep. It also includes off-talk examples for diagnosing unnecessary yielding and introduces pair accuracy, which grants no credit to constant-action policies. Experiments across multiple full-duplex systems show most exhibit a pronounced Yield bias, performing substantially better on interruptions than backchannels. The benchmark and metadata will be publicly released.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 15:58 UTC · 1d agoAI research

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The Machines Are Calling: Measuring Automated and Synthetic Voices in Unwanted Inbound Calls

Voice honeypot measurement finds at least 26.9% of unwanted US inbound calls open with machine voices, 13.1% with fresh synthetic speech.

An interactive voice honeypot using language-model personas on real US numbers recorded 10,987 calls over 66 days, following the FCC's February 2024 ruling that AI-generated voices fall under the TCPA. Of 7,233 greeted calls, 13.8% opened with recordings replayed from other calls and 13.1% with fresh audio labeled synthetic, with replays making up 45% of the detector's flagged rate. Synthetic openings concentrated in lead-generation spam (33.8%) rather than fraud (21.1%), and only 0.44% of calls disclosed automation. Prevalence tracked how long a bait number had circulated, and campaigns outlasted their numbers, with one synthetic voice serving nine campaigns.

arXiv cs.CR·Sep 10, 06:28 UTC · 6d agoResearch

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Kraken: LLM-based Speech-to-Speech Translation via Low-bitrate VQ and Dual-path Source Conditioning

Kraken speech-to-speech translation model builds on Qwen3-8B with low-bitrate vector quantization and source-conditioned vocoding.

Kraken augments a pre-trained Qwen3-8B LLM with speech feature inputs and low-bitrate single-layer vector-quantized tokens trained to reconstruct SSL features. A separate token-to-waveform decoder, Autowave-X, is conditioned on source speech to improve non-linguistic transfer. Training used 150k hours of multilingual and multitask speech data. The model reportedly beats SeamlessM4T-Large v2 and Qwen2.5-Omni in translation quality and speaker/prosody transfer.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:41 UTC · 5d agoAI research1

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X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation

XPeng AI's X-AuT prunes speech LLM audio encoders, cutting Qwen3-ASR-0.6B error from 5.61% to 5.27% with fewer parameters.

X-AuT is a progressive compression framework for speech LLM audio encoders that selects layer combinations via short behavioral probes and restores pruned models using cross-scale distillation and LoRA finetuning while keeping the language-model backbone frozen. Compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers lowered macro-average error from 5.61% to 5.27% on ten Chinese-English benchmarks. A 14-layer model reached 5.75% error with 20.7% fewer audio-tower parameters, and progressive pruning outperformed direct pruning (5.75% vs 6.73%).

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research

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Apple Watch’s new AI features are normalizing the idea that technology is always listening

TechCrunch argues Apple Watch's Live Rewind and Siri Recap normalize always-listening AI, raising consent and legal questions despite privacy safeguards.

Analysis of Apple's new Apple Watch AI features contends that Live Rewind, which transcribes the previous 15 seconds of audio, and Siri Recap, which generates high-level conversation notes, are pushing consumers toward accepting always-listening technology. The piece acknowledges the accessibility value of on-device Audio Intelligence, which alerts deaf or hard-of-hearing users to sounds like sirens, alarms, doorbells, and crying babies. It also raises concerns about consent, the evidentiary status of text-only transcripts in court, and cultural effects of pervasive capture, noting competitors like Friend, Amazon's Bee, and Plaud in the AI transcription space.

TechCrunch · AI·Sep 9, 20:24 UTC · 7d agoAI industry

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microsoft/VibeVoice-ASR-Streaming-7B — new model trending #27 on Hugging Face

Microsoft released VibeVoice-ASR-Streaming-7B, an open streaming ASR model with speaker attribution, custom hotwords, and support for 10 languages under MIT license.

Microsoft Research released VibeVoice-ASR-Streaming-7B on Hugging Face, a unified streaming speech recognition model that continuously transcribes who said what as speech arrives. The 7B model supports customized hotwords for domain-specific terms and 10 languages including Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. Code is available at github.com/microsoft/VibeVoice with a live demo, and a technical report is on arXiv (2609.02812). The model is licensed under MIT.

Hugging Face trending models·Sep 2, 15:46 UTC · 14d agoModel release1

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Amazon Prime Video’s new AI tech matches lips to dubbed audio

Amazon Prime Video launched AI lip-sync dubbing aligning actors' mouth movements with human-dubbed audio, starting with the English dub of Maxton Hall.

Prime Video's new feature combines AI and visual effects technologies to make lip movements match translated speech, currently available for seasons 1-2 of the German series Maxton Hall worldwide and planned for additional titles, with season 3 arriving December 9. Amazon previously piloted AI-aided dubbing in English and Spanish across 12 movies and shows last year. Meta and YouTube have recently launched similar AI auto-dubbing features with optional lip sync for creators.

The Verge · AI·Sep 9, 13:00 UTC · 7d agoAI industry

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Vidu S2: Real-Time Interactive, Editable, and Spatial Video Generation

Vidu S2 delivers real-time 720p interactive avatar generation plus real-time video editing with style, clothing, character, and background replacement.

Vidu S2 comprises Vidu S2-Avatar, a real-time interactive digital-character model, and Vidu S2-Editing, a real-time video editing model. S2-Avatar supports real-time 720p generation, dynamic references updatable at any moment, and stronger instruction following such as dancing, while S2-Editing performs real-time style rendering, clothing replacement, character replacement, and background replacement. The work also explores real-time spatial video generation for both models, reports outperforming all baselines, and offers a playable online demo at vidu.com.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoModel release

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Intelligent transcription with Gemini 3.5 Transcribe

Google DeepMind launched Gemini 3.5 Transcribe, a speech-to-text model offering more intelligent transcription as part of the Gemini family.

Google DeepMind announced Gemini 3.5 Transcribe, a new speech-to-text model described as delivering more intelligent transcription. The blog post provides limited technical detail in the available text, with no benchmarks or model sizes given. The release adds a dedicated audio transcription model to the Gemini family.

Google DeepMind·Aug 26, 17:01 UTC · 21d agoModel release

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Deep-Fake CAPTCHA: Mitigating Next-Generation Social Engineering Attacks

Researchers propose DF-CAPTCHA, a challenge-response defense that verifies callers in voice and video to defeat real-time deepfake impersonation in social engineering.

The DF-CAPTCHA framework prompts call participants with simple challenge-response tasks that are easy for humans but hard for real-time deepfake systems to convincingly generate. Responses are verified on four criteria: realism, identity consistency, task completion, and response time. User studies and experiments with real-time deepfake models across audio and video modalities show substantially improved detection over passive artifact-based methods.

arXiv cs.CR·Sep 10, 11:35 UTC · 6d agoResearch1

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