VideoFDB is a new benchmark and LM-as-judge framework for evaluating full-duplex audio-visual-to-audio-visual conversational agents on nonverbal dynamics from real video calls.
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Moshi: a speech-text foundation model for real-time dialogue
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abstract
We introduce Moshi, a speech-text foundation model and full-duplex spoken dialogue framework. Current systems for spoken dialogue rely on pipelines of independent components, namely voice activity detection, speech recognition, textual dialogue and text-to-speech. Such frameworks cannot emulate the experience of real conversations. First, their complexity induces a latency of several seconds between interactions. Second, text being the intermediate modality for dialogue, non-linguistic information that modifies meaning -- such as emotion or non-speech sounds -- is lost in the interaction. Finally, they rely on a segmentation into speaker turns, which does not take into account overlapping speech, interruptions and interjections. Moshi solves these independent issues altogether by casting spoken dialogue as speech-to-speech generation. Starting from a text language model backbone, Moshi generates speech as tokens from the residual quantizer of a neural audio codec, while modeling separately its own speech and that of the user into parallel streams. This allows for the removal of explicit speaker turns, and the modeling of arbitrary conversational dynamics. We moreover extend the hierarchical semantic-to-acoustic token generation of previous work to first predict time-aligned text tokens as a prefix to audio tokens. Not only this "Inner Monologue" method significantly improves the linguistic quality of generated speech, but we also illustrate how it can provide streaming speech recognition and text-to-speech. Our resulting model is the first real-time full-duplex spoken large language model, with a theoretical latency of 160ms, 200ms in practice, and is available at https://github.com/kyutai-labs/moshi.
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- abstract We introduce Moshi, a speech-text foundation model and full-duplex spoken dialogue framework. Current systems for spoken dialogue rely on pipelines of independent components, namely voice activity detection, speech recognition, textual dialogue and text-to-speech. Such frameworks cannot emulate the experience of real conversations. First, their complexity induces a latency of several seconds between interactions. Second, text being the intermediate modality for dialogue, non-linguistic information that modifies meaning -- such as emotion or non-speech sounds -- is lost in the interaction. Fina
- background -✗✓ ✓ SpeechVerse [113] May 2024 Flan-T5-XL 3B EN Contin. -✗✓ ✓ GAMA [114] Jun 2024 LLaMA2-7B 7B EN Contin. 2.2M audio-caption pairs✗✓ ✓ Qwen2-Audio [18] Jul 2024 Qwen-7B 7B Multi. Contin. 520K Hrs audio✗✓ ✓ FunAudioLLM [115] Jul 2024 - - Multi. - -✗✓ ✓ Mini-Omni [116] Aug 2024 Qwen2-0.5B 0.5B - Discrete 8K Hrs speech + 2M text examples✓ ✓ ✓ Moshi [117] Sep 2024 Helium 7B EN Discrete 7M Hrs audio + 2.1T text tokens✓ ✓ ✓ LLaMA-Omni [118] Sep 2024 Llama-3.1-8B-Instruct 8B EN Contin. -✗✓ ✓ Parrot [
- background components: aspeech moduleto generate natural voice, and avisual moduleto synthesize realistic motion, including fa- 2 cial expressions and gestures, in either 2D or 3D form. Speech dialog research splits into two paradigms: mod- ular and end-to-end. Modular systems pair an LLM with a TTS/vocoder-LLM emits text or semantic tokens, TTS renders waveforms [19, 22-24, 74, 88, 128]. End-to-end voice agents integrate understanding and speech generation in a single model, tightly coupling semantics, pr
- baseline We compare WavCodec against repre- sentative low-bitrate neural codecs and tokenizers. These baselines can be roughly divided into two groups. The first group does not explicitly introduce semantic supervision, including SimCodec [60], BigCodec [56], and WavTokenizer [19]. The second group incorpo- rates semantic constraints from pretrained speech models, includ- ing Mimi [7], XY-Tokenizer [12], X-codec2 [61], and BiCodec [47]. While high-fidelity codecs such as DAC [24] and EnCodec [6] can achi
- background bines them with text tokens as input to the LLM back- bone. During audio tokenization, LALMs extract acoustic features from raw audio signals and then apply vector quantization techniques to derive discrete audio tokens. Meanwhile, the LLM backbone extends its vocabulary and embedding matrix to accommodate audio tokens. Instead of audio tokenization, thecontinuous-featurescheme [32]- [43] directly aligns audio and text inputs within a unified embedding space. Such LALMs project acoustic features
- baseline These methods are lightweight and fast, but primarily capture speech presence rather than communicative intent, making them prone to false triggers from backchannels, hesitations, or background noise. The second group introduces explicit turn prediction modules using learned models. Representative examples include Smart Turn, TEN Turn Detection, and Easy Turn [14]. The second ap- proach enhances conversational intent detection by leveraging learned models and text-based cues, making it more adap
- background This requires modeling both arXiv:2604.08363v1 [cs.SD] 9 Apr 2026 Conference'17, July 2017, Washington, DC, USA Xiaosu Su, Zihan Sun, Peilei Jia, and Jun Gao stable speaker timbre and turn-level expressive variation: the former is better characterized by speaker-level global conditioning, while the latter needs to be explicitly modeled at the current turn[7, 28, 40]. A central open problem, however, is how to preserve a satisfactorily designed timbre for ongoing generation. Once a desired voice
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SpeechCombine produces instruction-following SLMs via speech pre-training followed by direct weight combination with the text LLM instruction delta, without any speech instruction tuning.
FlexiSLM is the first spoken language model supporting dynamic and controllable frame rates on speech input and output, outperforming fixed-rate 7B models at high quality and enabling faster inference at lower rates like 6.25 Hz.
AudioCALM presents a continuous autoregressive framework with flow-matching prediction and A-MoME architecture that unifies speech, sound, and music generation while matching modality-specific state-of-the-art performance.
Interleaved SLMs implicitly transcribe spoken words to text tokens in middle layers (top candidate for 77% of data) before predicting in text space and returning to speech.
A survey proposing an L0-L3 architectural hierarchy, T×I×R interaction ontology, and IDLE/LISTEN/SPEAK/WAIT/DUAL decision state machine for full-duplex spoken dialogue systems, documenting a realization gap between architectural potential and observed behavior due to training data limits.
Instruction-based vector steering redirects temporal attention in LALMs to acoustically relevant regions, recovering queried sound event locations with 60.87-68.72% overlap accuracy without training.
FD-SLMs exhibit state inertia during abrupt interruptions that a training-free perception-vector steering intervention mitigates, lifting correctness from 28% to 45% and IWOR from 40% to 72% on the Zero-Buffer Benchmark.
X-Stream benchmark shows SOTA MLLMs score ~50% on concurrent multi-stream tasks and lack proactive ability, using a dual-verification pipeline to avoid single-stream bias.
PolySpeech-100 is a new benchmark for native-level speech comprehension across 110 linguistic variants that evaluates 22 models and reports E2E advantages on dialects, robustness gaps on low-resource languages, and degradation from Chain-of-Thought prompting.
PrefixMem encoder for Semantic IDs improves deepest-level accuracy by up to 46% relative and full-SID retrieval recall by up to 22% relative on Pinterest data across LLM families.
TokTalk trains a chunk-based conditional flow matching model on a new audio-token to 3D facial motion dataset to enable real-time expressive facial animation from Audio-LLM tokens with low overhead adaptation.
RealityTest is a human-grounded multilingual multimodal benchmark showing that only 31% of people ask AI identity directly and that suppression instructions plus question phrasing dominate disclosure behavior over model choice.
AffectCodec applies block-diagonal projections in residual FSQ to explicitly allocate bits to emotion and acoustic subspaces, combined with emotion conditioning, yielding better emotion preservation at low bitrates with competitive acoustic quality.
DuplexSLA introduces a three-channel full-duplex architecture that synchronizes continuous user audio, discrete assistant audio, and rate-limited textual actions inside a single backbone for native turn-taking and in-conversation tool use.
CodecAttack perturbs audio in codec latent space with multi-bitrate EoT to achieve 85.5% average ASR on Opus-compressed Audio LLMs versus under 26% for waveform baselines, with transfer to MP3 and AAC.
ClariCodec achieves 3.55% WER on LibriSpeech test-clean at 300 bps by RL fine-tuning the encoder for intelligibility, yielding a 23% relative WER reduction while preserving perceptual quality.
AffectCodec is an emotion-guided neural speech codec that preserves emotional cues during quantization while maintaining semantic fidelity and prosodic naturalness.
Channel fusion gives better semantic grounding and QA performance in full-duplex LLM dialogue but is vulnerable to context corruption during interruptions, while cross-attention routing is more robust at the cost of weaker integration.
VITA-QinYu is the first expressive end-to-end spoken language model supporting role-playing and singing alongside conversation, trained on 15.8K hours of data and outperforming prior models on expressiveness and conversational benchmarks.
LiVeAction is a lightweight asymmetric neural codec using an FFT-inspired encoder and variance-based training that outperforms generative tokenizers in rate-distortion while supporting real-time use on resource-constrained sensors across modalities.
PairAlign learns compact variable-length token sequences for audio via self-alignment on paired content-preserving views, achieving 55% fewer archive tokens than VQ while preserving edit-distance retrieval at 12.71 tokens/s.
LWS is a text-first paradigm for full-duplex speech LLMs that treats visible writing as a primary output channel alongside audio input and spoken response, implemented via token schema and synthetic per-second annotations.
citing papers explorer
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A Survey of Full-Duplex Spoken Dialogue Systems: Architectural Hierarchy, Interaction Ontology, and Decision State Machine
A survey proposing an L0-L3 architectural hierarchy, T×I×R interaction ontology, and IDLE/LISTEN/SPEAK/WAIT/DUAL decision state machine for full-duplex spoken dialogue systems, documenting a realization gap between architectural potential and observed behavior due to training data limits.
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Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models
Spoken language models exhibit style amnesia and fail to maintain instructed paralinguistic styles across multi-turn conversations, with explicit recall offering partial mitigation.
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MiniMind-O Technical Report: An Open Small-Scale Speech-Native Omni Model
MiniMind-O delivers a working 0.1B-scale open omni model with speech-native output, Thinker-Talker split, frozen encoders, and full release of code, checkpoints, and training data.