# DASB - Discrete Audio and Speech Benchmark

arXiv: https://arxiv.org/abs/2406.14294 · HTML: https://mlsanity.com/paper/2406.14294/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 94 of its 130 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 30 of 94)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [SoundStream: An End-to-End Neural Audio Codec](https://mlsanity.com/paper/2107.03312/) (2021)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [SALMONN: Towards Generic Hearing Abilities for Large Language Models](https://mlsanity.com/paper/2310.13289/) (2023)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Finite Scalar Quantization: VQ-VAE Made Simple](https://mlsanity.com/paper/2309.15505/) (2023)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)

## Built on by (most cited first, 30 of 32)

- [On The Landscape of Spoken Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2504.08528/) (2025)
- [Open-Source Conversational AI with SpeechBrain 1.0](https://mlsanity.com/paper/2407.00463/) (2024)
- [Recent Advances in Discrete Speech Tokens: A Review](https://mlsanity.com/paper/2502.06490/) (2025)
- [M-Prometheus: A Suite of Open Multilingual LLM Judges](https://mlsanity.com/paper/2504.04953/) (2025)
- [FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks](https://mlsanity.com/paper/2502.04465/) (2025)
- [Protein Structure Tokenization: Benchmarking and New Recipe](https://mlsanity.com/paper/2503.00089/) (2025)
- [dMel: Speech Tokenization made Simple](https://mlsanity.com/paper/2407.15835/) (2024)
- [TSELM: Target Speaker Extraction using Discrete Tokens and Language Models](https://mlsanity.com/paper/2409.07841/) (2024)
- [DC-Spin: A Speaker-invariant Speech Tokenizer for Spoken Language Models](https://mlsanity.com/paper/2410.24177/) (2024)
- [FocalCodec-Stream: Streaming Low-Bitrate Speech Coding via Causal Distillation](https://mlsanity.com/paper/2509.16195/) (2025)
- [LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec](https://mlsanity.com/paper/2410.15764/) (2024)
- [PAST: Phonetic-Acoustic Speech Tokenizer](https://mlsanity.com/paper/2505.14470/) (2025)
- [LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models](https://mlsanity.com/paper/2504.07402/) (2025)
- [StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs](https://mlsanity.com/paper/2509.22220/) (2025)
- [Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization](https://mlsanity.com/paper/2601.23174/) (2026)
- [LinearVC: Linear transformations of self-supervised features through the lens of voice conversion](https://mlsanity.com/paper/2506.01510/) (2025)
- [Codec2Vec: Self-Supervised Speech Representation Learning Using Neural Speech Codecs](https://mlsanity.com/paper/2511.16639/) (2025)
- [Scaling Speech Tokenizers with Diffusion Autoencoders](https://mlsanity.com/paper/2602.06602/) (2026)
- [Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model](https://mlsanity.com/paper/2506.04518/) (2025)
- [Autoregressive Speech Enhancement via Acoustic Tokens](https://mlsanity.com/paper/2507.12825/) (2025)
- [An Empirical Analysis of Discrete Unit Representations in Speech Language Modeling Pre-training](https://mlsanity.com/paper/2509.05359/) (2025)
- [Do Neural Codecs Generalize? A Controlled Study Across Unseen Languages and Non-Speech Tasks](https://mlsanity.com/paper/2601.12205/) (2026)
- [Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine](https://mlsanity.com/paper/2507.12701/) (2025)
- [AudioCodecBench: A Comprehensive Benchmark for Audio Codec Evaluation](https://mlsanity.com/paper/2509.02349/) (2025)
- [Recovering Performance in Speech Emotion Recognition from Discrete Tokens via Multi-Layer Fusion and Paralinguistic Feature Integration](https://mlsanity.com/paper/2601.17085/) (2026)
- [Frontend Token Enhancement for Token-Based Speech Recognition](https://mlsanity.com/paper/2602.04217/) (2026)
- [STACodec: Semantic Token Assignment for Balancing Acoustic Fidelity and Semantic Information in Audio Codecs](https://mlsanity.com/paper/2602.06180/) (2026)
- [UniAudio-Token: Empowering Semantic Speech Tokenizers with General Audio Perception](https://mlsanity.com/paper/2605.31521/) (2026)
- [HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models](https://mlsanity.com/paper/2606.27627/) (2026)
- [ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding](https://mlsanity.com/paper/2609.11642/) (2026)

## Abstract (arXiv)

Discrete audio tokens have recently gained considerable attention for their potential to bridge audio and language processing, enabling multimodal language models that can both generate and understand audio. However, preserving key information such as phonetic content, speaker identity, and paralinguistic cues remains a major challenge. Identifying the optimal tokenizer and configuration is further complicated by inconsistent evaluation settings across existing studies. To address this, we introduce the Discrete Audio and Speech Benchmark (DASB), a comprehensive framework for benchmarking discrete audio tokens across speech, general audio, and music domains on a range of discriminative and generative tasks. Our results show that discrete representations are less robust than continuous ones and require careful tuning of factors such as model architecture, data size, learning rate, and capacity. Semantic tokens generally outperform acoustic tokens, but a gap remains between discrete tokens and continuous features, highlighting the need for further research. DASB codes, evaluation setup, and leaderboards are publicly available at https://poonehmousavi.github.io/DASB-website/.