XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs

• URL: http://arxiv.org/abs/2506.23325v2
• Date: Wed, 09 Jul 2025 17:40:35 GMT
• Title: XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs
• Abstract summary: Existing speech codecs struggle to balance high-quality audio reconstruction with ease of modeling by language models.<n>We propose XY-Tokenizer, a novel that mitigates the conflict between semantic and acoustic capabilities through multi-stage, multi-task learning.
• Score: 45.655747597751706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech codecs serve as bridges between speech signals and large language models. An ideal codec for speech language models should not only preserve acoustic information but also capture rich semantic information. However, existing speech codecs struggle to balance high-quality audio reconstruction with ease of modeling by language models. In this study, we analyze the limitations of previous codecs in balancing semantic richness and acoustic fidelity. We propose XY-Tokenizer, a novel codec that mitigates the conflict between semantic and acoustic capabilities through multi-stage, multi-task learning. Experimental results demonstrate that XY-Tokenizer achieves performance in both semantic and acoustic tasks comparable to that of state-of-the-art codecs operating at similar bitrates, even though those existing codecs typically excel in only one aspect. Specifically, XY-Tokenizer achieves strong text alignment, surpassing distillation-based semantic modeling methods such as SpeechTokenizer and Mimi, while maintaining a speaker similarity score of 0.83 between reconstructed and original audio. The reconstruction performance of XY-Tokenizer is comparable to that of BigCodec, the current state-of-the-art among acoustic-only codecs, which achieves a speaker similarity score of 0.84 at a similar bitrate. Code and models are available at https://github.com/gyt1145028706/XY-Tokenizer.

Related papers

• EntangleCodec: A Unified Discrete Audio Tokenizer via Semantic-Acoustic Entanglement [54.95940885045993]
  EntangleCodec is a unified discrete audio tokenizer that learns caption-aligned semantic-acoustic representations before quantization.<n>It captures linguistic content, speaker identity, emotion, prosody, and acoustic scenes within a compact token stream.<n>It achieves reconstruction quality competitive with specialized codecs, and supports both TTS and TTA generation in a unified framework.
  arXiv  Detail & Related papers  (2026-06-01T18:05:18Z)
• UniAudio-Token: Empowering Semantic Speech Tokenizers with General Audio Perception [53.41256816230278]
  UniAudio-Token is a framework that empowers semantic tokenizers with general audio perception without compromising speech ability.<n>SAP provide structured supervision by audio into linguistic content, vocal attributes, and auditory-scene primitives.<n>SAE introduces a content-aware gating mechanism that adaptively restores fine-grained acoustic details from shallow layers.
  arXiv  Detail & Related papers  (2026-05-29T16:36:21Z)
• X-VC: Zero-shot Streaming Voice Conversion in Codec Space [68.11390597559101]
  X-VC is a zero-shot streaming VC system that performs one-step conversion in the latent space of a pretrained neural.<n>X-VC achieves the best streaming WER in both English and Chinese, strong speaker similarity in same-latency and cross-lingual settings.
  arXiv  Detail & Related papers  (2026-04-14T08:42:10Z)
• TaDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling [13.05578634768109]
  We introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec)<n>TaDiCodec employs end-to-end optimization for quantization and reconstruction through a diffusion autoencoder.<n>It achieves an extremely low frame rate of 6.25 Hz and corresponding compression of 0.0875 kbps with a single-layer codebook for 24 kHz speech.
  arXiv  Detail & Related papers  (2025-08-22T20:45:03Z)
• SecoustiCodec: Cross-Modal Aligned Streaming Single-Codecbook Speech Codec [83.61175662066364]
  Speech codecs serve as a crucial bridge in unifying speech and text language models.<n>Existing methods face several challenges in semantic encoding.<n>We propose SecoustiCodec, a cross-modal aligned low-bitrate streaming speech codecs.
  arXiv  Detail & Related papers  (2025-08-04T19:22:14Z)
• HH-Codec: High Compression High-fidelity Discrete Neural Codec for Spoken Language Modeling [6.313337261965531]
  We introduce HH-Codec, a neural codecs that achieves extreme compression at 24 tokens per second for 24 kHz audio.<n>Our approach involves a carefully designed Vector Quantization space for Spoken Language Modeling, optimizing compression efficiency while minimizing information loss.<n> HH-Codec achieves state-of-the-art performance in speech reconstruction with an ultra-low bandwidth of 0.3 kbps.
  arXiv  Detail & Related papers  (2025-07-25T02:44:30Z)
• Towards Efficient Speech-Text Jointly Decoding within One Speech Language Model [76.06585781346601]
  Speech language models (Speech LMs) enable end-to-end speech-text modelling within a single model.<n>The choice of speech-text jointly decoding paradigm plays a critical role in performance, efficiency, and alignment quality.
  arXiv  Detail & Related papers  (2025-06-04T23:53:49Z)
• FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks [12.446324804274628]
  FocalCodec is an efficient low-bitrate based on focal modulation that utilizes a single binary codebook to compress speech.<n>Demo samples, code and checkpoints are available at https://lucadellalib.io/focalcodec-web/.
  arXiv  Detail & Related papers  (2025-02-06T19:24:50Z)
• LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec [14.7377193484733]
  We propose LSCodec, a discrete speech that has both low and speaker decoupling ability.<n>By reconstruction evaluations, LSCodec demonstrates superior intelligibility and audio quality with only a single codebook and smaller vocabulary size than baselines.
  arXiv  Detail & Related papers  (2024-10-21T08:23:31Z)
• Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model [36.61105228468503]
  X-Codec incorporates semantic features from a pre-trained semantic encoder before the Residual Vector Quantization stage.<n>X-Codec significantly reduces WER in speech synthesis tasks and extends these benefits to non-speech applications.<n>Our experiments in text-to-speech, music continuation, and text-to-sound tasks demonstrate that integrating semantic information substantially improves the overall performance of language models in audio generation.
  arXiv  Detail & Related papers  (2024-08-30T10:24:07Z)
• WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling [63.8735398698683]
  A crucial component of language models is the tokenizer, which compresses high-dimensional natural signals into lower-dimensional discrete tokens.<n>We introduce WavTokenizer, which offers several advantages over previous SOTA acoustic models in the audio domain.<n>WavTokenizer achieves state-of-the-art reconstruction quality with outstanding UTMOS scores and inherently contains richer semantic information.
  arXiv  Detail & Related papers  (2024-08-29T13:43:36Z)
• CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstruction [61.067153685104394]
  Dysarthric speech reconstruction (DSR) aims to transform dysarthric speech into normal speech. It still suffers from low speaker similarity and poor prosody naturalness. We propose a multi-modal DSR model by leveraging neural language modeling to improve the reconstruction results.
  arXiv  Detail & Related papers  (2024-06-12T15:42:21Z)
• SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound [40.810505707522324]
  SemantiCodec is designed to compress audio into fewer than a hundred tokens per second across diverse audio types.<n>We show that SemantiCodec significantly outperforms the state-of-the-art Descript on reconstruction quality.<n>Our results also suggest that SemantiCodec contains significantly richer semantic information than all evaluated state-of-the-art audio codecs.
  arXiv  Detail & Related papers  (2024-04-30T22:51:36Z)
• Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data [145.95460945321253]
  We introduce two pre-training tasks for the encoder-decoder network using acoustic units, i.e., pseudo codes. The proposed Speech2C can relatively reduce the word error rate (WER) by 19.2% over the method without decoder pre-training.
  arXiv  Detail & Related papers  (2022-03-31T15:33:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.