:trident: Speech Trident - Awesome Speech LM
June 29, 2026 · View on GitHub
In this repository, we survey three crucial areas: (1) representation learning, (2) neural codec, and (3) language models that contribute to speech/audio large language models.
1.⚡ Speech Representation Models: These models focus on learning structural speech representations, which can then be quantized into discrete speech tokens, often refer to semantic tokens.
2.⚡ Speech Neural Codec Models: These models are designed to learn speech and audio discrete tokens, often referred to as acoustic tokens, while maintaining reconstruction ability and low bitrate.
3.⚡ Speech Large Language Models: These models are trained on top of speech and acoustic tokens in a language modeling approach. They demonstrate proficiency in tasks on speech understanding and speech generation.
:trident: Contributors
Kai-Wei Chang |
Haibin Wu |
Wei-Cheng Tseng |
Kehan Lu |
Chun-Yi Kuan |
Hung-yi Lee |
:trident: 2026 News
New survey repo for spoken dialogue models is available on GitHub
@article{chang2026tico,
title={TiCo: Time-Controllable Training for Spoken Dialogue Models},
author={Kai-Wei Chang and Wei-Chih Chen and En-Pei Hu and Hung-yi Lee and James Glass},
journal={arXiv preprint arXiv:2603.22267},
year={2026}
}
:trident: 2025 News
The survey paper “On The Landscape of Spoken Language Models: A Comprehensive Survey” is now available on arXiv
@article{arora2025landscape,
title={On The Landscape of Spoken Language Models: A Comprehensive Survey},
author={Arora, Siddhant and Chang, Kai-Wei and Chien, Chung-Ming and Peng, Yifan and Wu, Haibin and Adi, Yossi and Dupoux, Emmanuel and Lee, Hung-Yi and Livescu, Karen and Watanabe, Shinji},
journal={arXiv preprint arXiv:2504.08528},
year={2025}
}
It provides a comprehensive survey of spoken language models (SLMs), which covers a lot of speech/audio langauge models surveyed in this Speech-Trident project. But with more detailed and technical discussion. The paper categorizes the SLMs into:
1.⚡ Pure Speech LM
2.⚡ Speech-aware Text LM
3.⚡ Speech + Text LM
In addition, the paper discusses training strategies, speech/text token decoding patterns, duplex speech dialogue, benchmarks for SLMs, and more. Please read the paper for more details. You will enjoy it!!
:trident: Speech/Audio Language Models
| Date | Model Name | Paper Title | Link |
|---|---|---|---|
| 2025-07 | Audio Flamingo 3 | Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models | paper |
| 2025-07 | DeSTA2.5-Audio | DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment | paper |
| 2025-05 | SLED | Efficient Speech Language Modeling via Energy Distance in Continuous Latent Space | paper |
| 2025-05 | BALSa | From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data | paper |
| 2025-05 | Fun-ASR-Nano | Fun-ASR-Nano: An LLM-based Non-autoregressive End-to-End Speech Recognition Model | paper, code |
| 2025-04 | - | On The Landscape of Spoken Language Models: A Comprehensive Survey | Paper |
| 2025-04 | Kimi-Audio | Kimi-Audio Technical Report | Paper |
| 2025-03 | Qwen2.5-Omni | Qwen2.5-Omni Technical Report | Paper |
| 2025-03 | Yue | YuE: Scaling Open Foundation Models for Long-Form Music Generation | Paper |
| 2025-03 | CSM | Conversational speech generation | blog |
| 2025-03 | Phi-4-Multimodal | Phi-4-Mini Technical Report: compact yet powerful multimodal language models via mixture-of-LoRAs | paper |
| 2025-03 | Baichuan-Audio | Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction | paper |
| 2025-02 | DiTAR | DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation | paper |
| 2025-02 | Slamming | Slamming: Training a Speech Language Model on One GPU in a Day | paper |
| 2025-02 | Step-Audio | Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction | paper |
| 2025-01 | BAICHUAN-OMNI-1.5 | BAICHUAN-OMNI-1.5 TECHNICAL REPORT | paper |
| 2025-01 | MiniCPM-o | A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone | GitHub |
| 2025-01 | MinMo | MinMo: A Multimodal large Language Model for Seamless Voice Interaction | Paper |
| 2025-01 | VITA-1.5 | VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction | Paper |
| 2025-01 | OMNICHAT | OmniChat: Enhancing Spoken Dialogue Systems with Scalable Synthetic Data for Diverse Scenarios | Paper |
| 2025-01 | SLIDE | SLIDE: Integrating Speech Language Model with LLM for Spontaneous Spoken Dialogue Generation | Paper |
| 2024-12 | SLAM-Omni | SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training | paper, code |
| 2024-12 | TouchTTS | TouchTTS: An Embarrassingly Simple TTS Framework that Everyone Can Touch | Paper |
| 2024-12 | CosyVoice 2 | CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models | Paper |
| 2024-12 | GLM-4-Voice | GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot | Paper |
| 2024-12 | AlignFormer | AlignFormer: Modality Matching Can Achieve Better Zero-shot Instruction-Following Speech-LLM | Paper |
| 2024-11 | -- | Scaling Speech-Text Pre-training with Synthetic Interleaved Data | Paper |
| 2024-11 | -- | State-Space Large Audio Language Models | Paper |
| 2024-11 | -- | Building a Taiwanese Mandarin Spoken Language Model: A First Attempt | Paper |
| 2024-11 | Ultravox | Ultravox: An open-weight alternative to GPT-4o Realtime | Blog |
| 2024-11 | hertz-dev | blog | GitHub |
| 2024-11 | Freeze-Omni | Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM | paper |
| 2024-11 | Align-SLM | Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback | paper |
| 2024-10 | Ichigo | Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant | paper, code |
| 2024-10 | OmniFlatten | OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation | paper |
| 2024-10 | GPT-4o | GPT-4o System Card | paper |
| 2024-10 | Baichuan-OMNI | Baichuan-Omni Technical Report | paper |
| 2024-10 | GLM-4-Voice | GLM-4-Voice | GitHub |
| 2024-10 | -- | Roadmap towards Superhuman Speech Understanding using Large Language Models | paper |
| 2024-10 | SALMONN-OMNI | SALMONN-OMNI: A SPEECH UNDERSTANDING AND GENERATION LLM IN A CODEC-FREE FULL-DUPLEX FRAMEWORK | paper |
| 2024-10 | Mini-Omni 2 | Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities | paper |
| 2024-10 | HALL-E | HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis | paper |
| 2024-10 | SyllableLM | SyllableLM: Learning Coarse Semantic Units for Speech Language Models | paper |
| 2024-09 | DeSTA 2 | DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data | paper |
| 2024-09 | Moshi | Moshi: a speech-text foundation model for real-time dialogue | paper |
| 2024-09 | Takin AudioLLM | Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models | paper |
| 2024-09 | FireRedTTS | FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications | paper |
| 2024-09 | LLaMA-Omni | LLaMA-Omni: Seamless Speech Interaction with Large Language Models | paper |
| 2024-09 | MaskGCT | MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer | paper |
| 2024-09 | SSR-Speech | SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis | paper |
| 2024-09 | MoWE-Audio | MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoders | paper |
| 2024-08 | Mini-Omni | Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | paper |
| 2024-08 | Make-A-Voice 2 | Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learner | paper |
| 2024-08 | LSLM | Language Model Can Listen While Speaking | paper |
| 2024-07 | SenseVoice | SenseVoice: Multilingual Speech Understanding with Multi-Task Speech Foundation Model | paper, code |
| 2024-07 | Seed-ASR | Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition | paper |
| 2024-07 | MELLE | Autoregressive Speech Synthesis without Vector Quantization | paper |
| 2024-06 | SimpleSpeech | SimpleSpeech: Towards Simple and Efficient Text-to-Speech with Scalar Latent Transformer Diffusion Models | paper |
| 2024-06 | UniAudio 1.5 | UniAudio 1.5: Large Language Model-driven Audio Codec is A Few-shot Audio Task Learner | paper |
| 2024-06 | VALL-E R | VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment | paper |
| 2024-06 | VALL-E 2 | VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers | paper |
| 2024-06 | GPST | Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer | paper |
| 2024-04 | CLaM-TTS | CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech | paper |
| 2024-04 | RALL-E | RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis | paper |
| 2024-04 | WavLLM | WavLLM: Towards Robust and Adaptive Speech Large Language Model | paper |
| 2024-02 | MobileSpeech | MobileSpeech: A Fast and High-Fidelity Framework for Mobile Zero-Shot Text-to-Speech | paper |
| 2024-02 | SLAM-ASR | An Embarrassingly Simple Approach for LLM with Strong ASR Capacity | paper |
| 2024-02 | AnyGPT | AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling | paper |
| 2024-02 | SpiRit-LM | SpiRit-LM: Interleaved Spoken and Written Language Model | paper |
| 2024-02 | USDM | Integrating Paralinguistics in Speech-Empowered Large Language Models for Natural Conversation | paper |
| 2024-02 | BAT | BAT: Learning to Reason about Spatial Sounds with Large Language Models | paper |
| 2024-02 | Audio Flamingo | Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities | paper |
| 2024-02 | Text Description to speech | Natural language guidance of high-fidelity text-to-speech with synthetic annotations | paper |
| 2024-02 | GenTranslate | GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators | paper |
| 2024-02 | Base-TTS | BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data | paper |
| 2024-02 | -- | It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition | paper |
| 2024-01 | -- | Large Language Models are Efficient Learners of Noise-Robust Speech Recognition | paper |
| 2024-01 | ELLA-V | ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering | paper |
| 2023-12 | Seamless | Seamless: Multilingual Expressive and Streaming Speech Translation | paper |
| 2023-11 | Qwen-Audio | Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models | paper |
| 2023-10 | LauraGPT | LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT | paper |
| 2023-10 | SALMONN | SALMONN: Towards Generic Hearing Abilities for Large Language Models | paper |
| 2023-10 | UniAudio | UniAudio: An Audio Foundation Model Toward Universal Audio Generation | paper |
| 2023-10 | Whispering LLaMA | Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition | paper |
| 2023-09 | VoxtLM | Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks | paper |
| 2023-09 | LTU-AS | Joint Audio and Speech Understanding | paper |
| 2023-09 | SLM | SLM: Bridge the thin gap between speech and text foundation models | paper |
| 2023-09 | -- | Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting | paper |
| 2023-08 | SpeechGen | SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts | paper |
| 2023-08 | SpeechX | SpeechX: Neural Codec Language Model as a Versatile Speech Transformer | paper |
| 2023-08 | LLaSM | Large Language and Speech Model | paper |
| 2023-08 | SeamlessM4T | Massively Multilingual & Multimodal Machine Translation | paper |
| 2023-07 | Speech-LLaMA | On decoder-only architecture for speech-to-text and large language model integration | paper |
| 2023-07 | LLM-ASR(temp.) | Prompting Large Language Models with Speech Recognition Abilities | paper |
| 2023-06 | AudioPaLM | AudioPaLM: A Large Language Model That Can Speak and Listen | paper |
| 2023-05 | Make-A-Voice | Make-A-Voice: Unified Voice Synthesis With Discrete Representation | paper |
| 2023-05 | Spectron | Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLM | paper |
| 2023-05 | TWIST | Textually Pretrained Speech Language Models | paper |
| 2023-05 | Pengi | Pengi: An Audio Language Model for Audio Tasks | paper |
| 2023-05 | SoundStorm | Efficient Parallel Audio Generation | paper |
| 2023-05 | LTU | Joint Audio and Speech Understanding | paper |
| 2023-05 | SpeechGPT | Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities | paper |
| 2023-05 | VioLA | Unified Codec Language Models for Speech Recognition, Synthesis, and Translation | paper |
| 2023-05 | X-LLM | X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages | paper |
| 2023-03 | Google USM | Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages | paper |
| 2023-03 | VALL-E X | Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling | paper |
| 2023-02 | SPEAR-TTS | Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision | paper |
| 2023-01 | VALL-E | Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers | paper |
| 2022-12 | Whisper | Robust Speech Recognition via Large-Scale Weak Supervision | paper |
| 2022-10 | AudioGen | AudioGen: Textually Guided Audio Generation | paper |
| 2022-09 | AudioLM | AudioLM: a Language Modeling Approach to Audio Generation | paper |
| 2022-05 | Wav2Seq | Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages | paper |
| 2022-04 | Unit mBART | Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation | paper |
| 2022-03 | d-GSLM | Generative Spoken Dialogue Language Modeling | paper |
| 2021-10 | SLAM | SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training | paper |
| 2021-09 | p-GSLM | Text-Free Prosody-Aware Generative Spoken Language Modeling | paper |
| 2021-02 | GSLM | Generative Spoken Language Modeling from Raw Audio | paper |
:trident: Speech/Audio Codec Models
| Date | Model Name | Paper Title | Link |
|---|---|---|---|
| 2025-06 | CodecSlime | CodecSlime: Temporal Redundancy Compression of Neural Speech Codec via Dynamic Frame Rate | paper |
| 2025-06 | Discrete Audio Tokens: More Than a Survey! | paper | |
| 2025-06 | TaDiCodec | TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling | paper |
| 2025-06 | MagiCodec | MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation | paper |
| 2025-06 | - | Probing the Robustness Properties of Neural Speech Codecs | paper |
| 2025-06 | DS-Codec | DS-Codec: Dual-Stage Training with Mirror-to-NonMirror Architecture Switching for Speech Codec | paper |
| 2025-05 | LFSC | Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference | paper |
| 2025-05 | PAST | PAST: Phonetic-Acoustic Speech Tokenizer | paper |
| 2025-04 | ALMTokenizer | ALMTokenizer: A Low-bitrate and Semantic-rich Audio Codec Tokenizer for Audio Language Modeling | paper |
| 2025-04 | DualCodec | DualCodec: A Low-Frame-Rate, Semantically-Enhanced Neural Audio Codec for Speech Generation | paper |
| 2025-04 | - | One Quantizer is Enough: Toward a Lightweight Audio Codec | paper |
| 2025-04 | TASTE | TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling | paper |
| 2025-03 | UniCodec | Universal Speech Token Learning via Low-Bitrate Neural Codec and Pretrained Representations | paper |
| 2025-03 | BiCodec | Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens | paper |
| 2025-03 | FlowDec | FlowDec: A flow-based full-band general audio codec with high perceptual quality | paper |
| 2025-02 | UniCodec | UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook | paper |
| 2025-02 | Baichuan-Audio Tokenizer | Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction | paper |
| 2025-02 | - | Recent Advances in Discrete Speech Tokens: A Review | paper |
| 2025-02 | FocalCodec | FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks | paper |
| 2025-02 | - | Efficient Evaluation of Quantization-Effects in Neural Codecs | paper |
| 2025-02 | X-Codec 2 | Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis | paper |
| 2025-02 | ComplexDec | ComplexDec: A Domain-robust High-fidelity Neural Audio Codec with Complex Spectrum Modeling | paper |
| 2024-12 | TS3-Codec | TS3-Codec: Transformer-Based Simple Streaming Single Codec | paper |
| 2024-12 | FreeCodec | FreeCodec: A disentangled neural speech codec with fewer tokens | paper |
| 2024-12 | TAAE | Scaling Transformers for Low-Bitrate High-Quality Speech Coding | paper |
| 2024-11 | BEST-STD | BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term Detection | paper |
| 2024-11 | PyramidCodec | PyramidCodec: Hierarchical Codec for Long-form Music Generation in Audio Domain | paper |
| 2024-11 | UniCodec | Universal Speech Token Learning Via Low-Bitrate Neural Codec and Pretrained Representations | paper |
| 2024-11 | SimVQ | Addressing Representation Collapse in Vector Quantized Models with One Linear Layer | paper |
| 2024-11 | MDCTCodec | MDCTCodec: A Lightweight MDCT-based Neural Audio Codec towards High Sampling Rate and Low Bitrate Scenarios | paper |
| 2024-10 | APCodec+ | APCodec+: A Spectrum-Coding-Based High-Fidelity and High-Compression-Rate Neural Audio Codec with Staged Training Paradigm | paper |
| 2024-10 | - | A Closer Look at Neural Codec Resynthesis: Bridging the Gap between Codec and Waveform Generation | paper |
| 2024-10 | SNAC | SNAC: Multi-Scale Neural Audio Codec | paper |
| 2024-10 | LSCodec | LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec | paper |
| 2024-10 | Co-design for codec and codec-LM | TOWARDS CODEC-LM CO-DESIGN FOR NEURAL CODEC LANGUAGE MODELS | paper |
| 2024-10 | VChangeCodec | VChangeCodec: A High-efficiency Neural Speech Codec with Built-in Voice Changer for Real-time Communication | paper |
| 2024-10 | DC-Spin | DC-Spin: A Speaker-invariant Speech Tokenizer For Spoken Language Models | paper |
| 2024-10 | DM-Codec | DM-Codec: Distilling Multimodal Representations for Speech Tokenization | paper |
| 2024-09 | Mimi | Moshi: a speech-text foundation model for real-time dialogue | paper |
| 2024-09 | NDVQ | NDVQ: Robust Neural Audio Codec with Normal Distribution-Based Vector Quantization | paper |
| 2024-09 | SoCodec | SoCodec: A Semantic-Ordered Multi-Stream Speech Codec for Efficient Language Model Based Text-to-Speech Synthesis | paper |
| 2024-09 | BigCodec | BigCodec: Pushing the Limits of Low-Bitrate Neural Speech Codec | paper |
| 2024-08 | X-Codec | Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model | paper |
| 2024-08 | WavTokenizer | WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling | paper |
| 2024-07 | Super-Codec | SuperCodec: A Neural Speech Codec with Selective Back-Projection Network | paper |
| 2024-07 | dMel | dMel: Speech Tokenization made Simple | paper |
| 2024-06 | CodecFake | CodecFake: Enhancing Anti-Spoofing Models Against Deepfake Audios from Codec-Based Speech Synthesis Systems | paper |
| 2024-06 | Single-Codec | Single-Codec: Single-Codebook Speech Codec towards High-Performance Speech Generation | paper |
| 2024-06 | SQ-Codec | SimpleSpeech: Towards Simple and Efficient Text-to-Speech with Scalar Latent Transformer Diffusion Models | paper |
| 2024-06 | PQ-VAE | Addressing Index Collapse of Large-Codebook Speech Tokenizer with Dual-Decoding Product-Quantized Variational Auto-Encoder | paper |
| 2024-06 | LLM-Codec | UniAudio 1.5: Large Language Model-driven Audio Codec is A Few-shot Audio Task Learner | paper |
| 2024-05 | HILCodec | HILCodec: High Fidelity and Lightweight Neural Audio Codec | paper |
| 2024-04 | SemantiCodec | SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound | paper |
| 2024-04 | PromptCodec | PromptCodec: High-Fidelity Neural Speech Codec using Disentangled Representation Learning based Adaptive Feature-aware Prompt Encoders | paper |
| 2024-04 | ESC | ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers | paper |
| 2024-03 | FACodec | NaturalSpeech 3: Zero-Shot Speech Synthesis with Factorized Codec and Diffusion Models | paper |
| 2024-02 | AP-Codec | APCodec: A Neural Audio Codec with Parallel Amplitude and Phase Spectrum Encoding and Decoding | paper |
| 2024-02 | Language-Codec | Language-Codec: Reducing the Gaps Between Discrete Codec Representation and Speech Language Models | paper |
| 2024-01 | ScoreDec | ScoreDec: A Phase-preserving High-Fidelity Audio Codec with A Generalized Score-based Diffusion Post-filter | paper |
| 2023-11 | HierSpeech++ | HierSpeech++: Bridging the Gap between Semantic and Acoustic Representation of Speech by Hierarchical Variational Inference for Zero-shot Speech Synthesis | paper |
| 2023-10 | TiCodec | FEWER-TOKEN NEURAL SPEECH CODEC WITH TIME-INVARIANT CODES | paper |
| 2023-09 | RepCodec | RepCodec: A Speech Representation Codec for Speech Tokenization | paper |
| 2023-09 | FunCodec | FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec | paper |
| 2023-08 | SpeechTokenizer | Speechtokenizer: Unified speech tokenizer for speech large language models | paper |
| 2023-06 | VOCOS | VOCOS: CLOSING THE GAP BETWEEN TIME-DOMAIN AND FOURIER-BASED NEURAL VOCODERS FOR HIGH-QUALITY AUDIO SYNTHESIS | paper |
| 2023-06 | Descript-audio-codec | High-Fidelity Audio Compression with Improved RVQGAN | paper |
| 2023-05 | AudioDec | Audiodec: An open-source streaming highfidelity neural audio codec | paper |
| 2023-05 | HiFi-Codec | Hifi-codec: Group-residual vector quantization for high fidelity audio codec | paper |
| 2023-03 | LMCodec | LMCodec: A Low Bitrate Speech Codec With Causal Transformer Models | paper |
| 2022-11 | Disen-TF-Codec | Disentangled Feature Learning for Real-Time Neural Speech Coding | paper |
| 2022-10 | EnCodec | High fidelity neural audio compression | paper |
| 2022-07 | S-TFNet | Cross-Scale Vector Quantization for Scalable Neural Speech Coding | paper |
| 2022-01 | TFNet | End-to-End Neural Speech Coding for Real-Time Communications | paper |
| 2021-07 | SoundStream | SoundStream: An End-to-End Neural Audio Codec | paper |
:trident: Speech/Audio Representation Models
| Date | Model Name | Paper Title | Link |
|---|---|---|---|
| 2025-06 | USAD | USAD: Universal Speech and Audio Representation via Distillation | paper |
| 2025-03 | UniWav | UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation | paper |
| 2024-09 | NEST-RQ | NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training | paper |
| 2024-01 | EAT | Self-Supervised Pre-Training with Efficient Audio Transformer | paper |
| 2023-10 | MR-HuBERT | Multi-resolution HuBERT: Multi-resolution Speech Self-Supervised Learning with Masked Unit Prediction | paper |
| 2023-10 | SpeechFlow | Generative Pre-training for Speech with Flow Matching | paper |
| 2023-09 | WavLabLM | Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning | paper |
| 2023-08 | W2v-BERT 2.0 | Massively Multilingual & Multimodal Machine Translation | paper |
| 2023-07 | Whisper-AT | Noise-Robust Automatic Speech Recognizers are Also Strong General Audio Event Taggers | paper |
| 2023-06 | ATST | Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks | paper |
| 2023-05 | SPIN | Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering | paper |
| 2023-05 | DinoSR | Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning | paper |
| 2023-05 | NFA | Self-supervised neural factor analysis for disentangling utterance-level speech representations | paper |
| 2022-12 | Data2vec 2.0 | Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language | paper |
| 2022-12 | BEATs | Audio Pre-Training with Acoustic Tokenizers | paper |
| 2022-11 | MT4SSL | MT4SSL: Boosting Self-Supervised Speech Representation Learning by Integrating Multiple Targets | paper |
| 2022-08 | DINO | Non-contrastive self-supervised learning of utterance-level speech representations | paper |
| 2022-07 | Audio-MAE | Masked Autoencoders that Listen | paper |
| 2022-04 | MAESTRO | Matched Speech Text Representations through Modality Matching | paper |
| 2022-03 | MAE-AST | Masked Autoencoding Audio Spectrogram Transformer | paper |
| 2022-03 | LightHuBERT | Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | paper |
| 2022-02 | Data2vec | A General Framework for Self-supervised Learning in Speech, Vision and Language | paper |
| 2021-10 | WavLM | WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing | paper |
| 2021-08 | W2v-BERT | Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training | paper |
| 2021-07 | mHuBERT | Direct speech-to-speech translation with discrete units | paper |
| 2021-06 | HuBERT | Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units | paper |
| 2021-03 | BYOL-A | Self-Supervised Learning for General-Purpose Audio Representation | paper |
| 2020-12 | DeCoAR2.0 | DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization | paper |
| 2020-07 | TERA | TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech | paper |
| 2020-06 | Wav2vec2.0 | wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations | paper |
| 2019-10 | APC | Generative Pre-Training for Speech with Autoregressive Predictive Coding | paper |
| 2018-07 | CPC | Representation Learning with Contrastive Predictive Coding | paper |
:trident: SLT 2024 Codec-SUPERB challenge
- The challenge covers nowday's neural audio codecs and speech / audio language models.
- Time: December 3 starting at 15:15
- Detailed agenda: https://codecsuperb.github.io/
More about SLT 2024 Codec-SUPERB challenge
- Keynote speakers
- Neil Zeghidour (Kyutai): 15:15-16:00
- Dongchao Yang (CUHK): 16:00-16:35
- Shang-Wen Li (Meta): 16:35-17:10
- Wenwu Wang (University of Surrey): 17:40-18:15
- Minje Kim (UIUC): 18:15-18:50
- Host
- Accepted papers (Recording)
- ESPnet-Codec: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speech
- Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
- Investigating neural audio codecs for speech language model-based speech generation
- Addressing Index Collapse of Large-Codebook Speech Tokenizer with Dual-Decoding Product-Quantized Variational Auto-Encoder
- MDCTCodec: A Lightweight MDCT-based Neural Audio Codec towards High Sampling Rate and Low Bitrate Scenarios
:trident: Interspeech 2024 Survey Talk
Professor Hung-Yi Lee will be giving a talk as part of the Interspeech 2024 survey talk titled Challenges in Developing Spoken Language Models. The topic will cover nowday's speech/audio large language models.
:trident: ICASSP 2024 Tutorial Information
I (Kai-Wei Chang) will be giving a talk as part of the ICASSP 2024 tutorial titled Parameter-Efficient and Prompt Learning for Speech and Language Foundation Models. The topic will cover nowday's speech/audio large language models. The slides from my presentation is available at https://kwchang.org/talks/. Please feel free to reach out to me for any discussions.
:trident: Interspeech 2026 Tutorial
Coming soon....
🔱 Related Repository
| Name | GitHub Repo | Paper |
|---|---|---|
| :fire: NEW! Spoken-Dialogue-Model-Survey | :fire: NEW! Link | :fire: NEW! Paper |
| Towards Holistic Evaluation of Large Audio-Language Models | Link | Paper |
| Large Audio Models | Link | - |
| Awesome Speech Generation | Link | - |
| Speech Prompts and Adapters | Link | - |
| Codec-SUPERB | Link | Paper |
| Awesome Neural Programming and Prompting | Link | - |
Citation
The survey paper “On The Landscape of Spoken Language Models: A Comprehensive Survey” is now available on arXiv
@article{arora2025landscape,
title={On The Landscape of Spoken Language Models: A Comprehensive Survey},
author={Arora, Siddhant and Chang, Kai-Wei and Chien, Chung-Ming and Peng, Yifan and Wu, Haibin and Adi, Yossi and Dupoux, Emmanuel and Lee, Hung-Yi and Livescu, Karen and Watanabe, Shinji},
journal={arXiv preprint arXiv:2504.08528},
year={2025}
}
Related articles by the core contributors:
@article{chang2026tico,
title={TiCo: Time-Controllable Training for Spoken Dialogue Models},
author={Kai-Wei Chang and Wei-Chih Chen and En-Pei Hu and Hung-yi Lee and James Glass},
journal={arXiv preprint arXiv:2603.22267},
year={2026}
}
@article{wu2024ts3,
title={TS3-Codec: Transformer-Based Simple Streaming Single Codec},
author={Wu, Haibin and Kanda, Naoyuki and Eskimez, Sefik Emre and Li, Jinyu},
journal={arXiv preprint arXiv:2411.18803},
year={2024}
}
@article{wu2024codec,
title={Codec-SUPERB@ SLT 2024: A lightweight benchmark for neural audio codec models},
author={Wu, Haibin and Chen, Xuanjun and Lin, Yi-Cheng and Chang, Kaiwei and Du, Jiawei and Lu, Ke-Han and Liu, Alexander H and Chung, Ho-Lam and Wu, Yuan-Kuei and Yang, Dongchao and others},
journal={arXiv preprint arXiv:2409.14085},
year={2024}
}
@inproceedings{wu-etal-2024-codec,
title = "Codec-{SUPERB}: An In-Depth Analysis of Sound Codec Models",
author = "Wu, Haibin and
Chung, Ho-Lam and
Lin, Yi-Cheng and
Wu, Yuan-Kuei and
Chen, Xuanjun and
Pai, Yu-Chi and
Wang, Hsiu-Hsuan and
Chang, Kai-Wei and
Liu, Alexander and
Lee, Hung-yi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.616",
doi = "10.18653/v1/2024.findings-acl.616",
pages = "10330--10348",
}
@article{wu2023speechgen,
title={Speechgen: Unlocking the generative power of speech language models with prompts},
author={Wu, Haibin and Chang, Kai-Wei and Wu, Yuan-Kuei and Lee, Hung-yi},
journal={arXiv preprint arXiv:2306.02207},
year={2023}
}
@article{wu2024towards,
title={Towards audio language modeling-an overview},
author={Wu, Haibin and Chen, Xuanjun and Lin, Yi-Cheng and Chang, Kai-wei and Chung, Ho-Lam and Liu, Alexander H and Lee, Hung-yi},
journal={arXiv preprint arXiv:2402.13236},
year={2024}
}