arXiv AI
SLM-SS: Token-Based Speech Separation with Speech Language Models
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arXiv Machine Learning
Sep 14
TokenMapper: A Step Toward Interoperable Speech Token Translation
TokenMapper is a framework that enables direct translation between different speech tokenizers, allowing heterogeneous speech models to communicate without converting tokens to waveform audio. It handles mismatched token spaces, including single and multi-codebook representations, while maintaining a shared effective token rate. Experiments on GLM-4-Voice, MiMi, and DualCodec show that TokenMapper achieves word error rates close to native reconstructions, comparable human MOS scores, and significantly reduces latency compared to waveform bridging.
arXiv AI
Jul 3
UniSE: A Unified Framework for Decoder-Only Autoregressive LM-Based Speech Enhancement
arXiv:2510. 20441v2 Announce Type: replace-cross Abstract: Neural audio codecs have largely promoted the application of language models (LMs) for speech applications.
arXiv AI
Jun 9
GenTSE: Enhancing Target Speaker Extraction via a Coarse-to-Fine Generative Language Model
arXiv:2512. 20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech.
arXiv AI
Jun 9
End-to-End Training for Discrete Token LLM based TTS System
arXiv:2606. 09234v1 Announce Type: cross Abstract: Recent state-of-the-art (SOTA) text-to-speech (TTS) systems typically adopt a cascaded pipeline consisting of a speech tokenizer, an autoregressive large language model (LLM), and a diffusion based flow-matching (FM) model, with these components trained independently.
arXiv AI
Jun 2
DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion
arXiv:2601. 09239v5 Announce Type: replace-cross Abstract: Speech tokenizers are a key building block of fully discrete Speech LLMs.
arXiv Machine Learning
Aug 10
Multi Codec Discrete Diffusion Model for Text Guided Speech Inpainting and Editing
arXiv:2608. 06424v1 Announce Type: cross Abstract: Speech recordings often contain missing, corrupted, or incorrect regions that must be reconstructed or modified without re-synthesizing the entire utterance.