DrVoice: Parallel Speech-Text Voice Conversation Model via Dual-Resolution Speech Representations

• URL: http://arxiv.org/abs/2506.09349v3
• Date: Tue, 28 Oct 2025 09:04:11 GMT
• Title: DrVoice: Parallel Speech-Text Voice Conversation Model via Dual-Resolution Speech Representations
• Authors: Chao-Hong Tan, Qian Chen, Wen Wang, Chong Deng, Qinglin Zhang, Luyao Cheng, Hai Yu, Xin Zhang, Xiang Lv, Tianyu Zhao, Chong Zhang, Yukun Ma, Yafeng Chen, Hui Wang, Jiaqing Liu, Xiangang Li, Jieping Ye,
• Abstract summary: DRVOICE-7B establishes new state-of-the-art (SOTA) on OpenAudioBench and Big Bench Audio benchmarks.<n>This paper presents DrVoice, a parallel speech-text voice conversation model based on joint autoregressive modeling.
• Score: 62.00227663434538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies on end-to-end (E2E) speech generation with large language models (LLMs) have attracted significant community attention, with multiple works extending text-based LLMs to generate discrete speech tokens. Existing E2E approaches primarily fall into two categories: (1) Methods that generate discrete speech tokens independently without incorporating them into the LLM's autoregressive process, resulting in text generation being unaware of concurrent speech synthesis. (2) Models that generate interleaved or parallel speech-text tokens through joint autoregressive modeling, enabling mutual modality awareness during generation. This paper presents DrVoice, a parallel speech-text voice conversation model based on joint autoregressive modeling, featuring dual-resolution speech representations. Notably, while current methods utilize mainly 12.5Hz input audio representation, our proposed dual-resolution mechanism reduces the input frequency for the LLM to 5Hz, significantly reducing computational cost and alleviating the frequency discrepancy between speech and text tokens and in turn better exploiting LLMs' capabilities. Experimental results demonstrate that DRVOICE-7B establishes new state-of-the-art (SOTA) on OpenAudioBench and Big Bench Audio benchmarks, while achieving performance comparable to the SOTA on VoiceBench and UltraEval-Audio benchmarks, making it a leading open-source speech foundation model in ~7B models.

Related papers

• From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training [19.396162898865864]
  Text-to-Talk (TtT) is a unified audio-text framework that integrates autoregressive (AR) text generation with non-autoregressive (NAR) audio diffusion in a single Transformer.<n>To support this hybrid generation paradigm, we design a modality-aware attention mechanism that enforces causal decoding for text.<n>During inference, TtT employs block-wise diffusion to synthesize audio in parallel while flexibly handling variable-length outputs.
  arXiv  Detail & Related papers  (2025-09-24T12:44:26Z)
• 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)
• VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language Model [84.25283710008785]
  VITA-Audio is an end-to-end large speech model with fast audio-text token generation.<n>MCTP module efficiently generates multiple audio tokens within a single model forward pass.<n>Four-stage progressive training strategy is explored to achieve model acceleration with minimal loss of speech quality.
  arXiv  Detail & Related papers  (2025-05-06T17:59:53Z)
• GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM [42.93855899824886]
  We propose a text-to-speech generation approach optimized via a novel dual-branch ArchiTecture (GOAT-TTS)<n>GOAT-TTS combines a speech encoder and projector to capture continuous acoustic embeddings, enabling bidirectional correlation between paralinguistic features (language, timbre, emotion) and semantic text representations without transcript dependency.<n> Experimental results demonstrate that our GOAT-TTS achieves performance comparable to state-of-the-art TTS models.
  arXiv  Detail & Related papers  (2025-04-15T01:44:56Z)
• SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation [10.828717295018123]
  We propose a unified embedding framework that eliminates the need for intermediate text representations.<n>Our model reduces pipeline latency by 50% while achieving higher retrieval accuracy compared to traditional two-stage methods.
  arXiv  Detail & Related papers  (2025-01-26T15:04:02Z)
• CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models [74.80386066714229]
  We present an improved streaming speech synthesis model, CosyVoice 2.<n>Specifically, we introduce finite-scalar quantization to improve codebook utilization of speech tokens.<n>We develop a chunk-aware causal flow matching model to support various synthesis scenarios.
  arXiv  Detail & Related papers  (2024-12-13T12:59:39Z)
• Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversation [46.93969003104427]
  This paper introduces an extensive speech-text LLM framework, the Unified Spoken Dialog Model (USDM)<n>USDM is designed to generate coherent spoken responses with naturally occurring prosodic features relevant to the given input speech.<n>Our approach effectively generates natural-sounding spoken responses, surpassing previous and cascaded baselines.
  arXiv  Detail & Related papers  (2024-02-08T14:35:09Z)
• Toward Joint Language Modeling for Speech Units and Text [89.32163954508489]
  We explore joint language modeling for speech units and text. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks.
  arXiv  Detail & Related papers  (2023-10-12T20:53:39Z)
• One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition [50.055765860343286]
  This paper presents a novel framework for joint speaker diarization and automatic speech recognition. The framework, named SLIDAR, can process arbitrary length inputs and can handle any number of speakers. Experiments performed on monaural recordings from the AMI corpus confirm the effectiveness of the method in both close-talk and far-field speech scenarios.
  arXiv  Detail & Related papers  (2023-10-02T23:03:30Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation [61.564874831498145]
  TranSpeech is a speech-to-speech translation model with bilateral perturbation. We establish a non-autoregressive S2ST technique, which repeatedly masks and predicts unit choices. TranSpeech shows a significant improvement in inference latency, enabling speedup up to 21.4x than autoregressive technique.
  arXiv  Detail & Related papers  (2022-05-25T06:34:14Z)
• Direct speech-to-speech translation with discrete units [64.19830539866072]
  We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. We propose to predict the self-supervised discrete representations learned from an unlabeled speech corpus instead. When target text transcripts are available, we design a multitask learning framework with joint speech and text training that enables the model to generate dual mode output (speech and text) simultaneously in the same inference pass.
  arXiv  Detail & Related papers  (2021-07-12T17:40:43Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)

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.