MOSS-TTSD: Text to Spoken Dialogue Generation

• URL: http://arxiv.org/abs/2603.19739v1
• Date: Fri, 20 Mar 2026 08:23:31 GMT
• Title: MOSS-TTSD: Text to Spoken Dialogue Generation
• Abstract summary: We present MOSS-TTSD, a spoken dialogue synthesis model for expressive, multi-party conversational speech across multiple languages.<n>With enhanced long-context modeling, MOSS-TTSD generates long-form spoken conversations from dialogue scripts with explicit speaker tags.<n>The model supports various mainstream languages, including English and Chinese, and is adapted to several long-form scenarios.
• Score: 62.04179716555789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spoken dialogue generation is crucial for applications like podcasts, dynamic commentary, and entertainment content, but poses significant challenges compared to single-utterance text-to-speech (TTS). Key requirements include accurate turn-taking, cross-turn acoustic consistency, and long-form stability, which current models often fail to address due to a lack of dialogue context modeling. To bridge this gap, we present MOSS-TTSD, a spoken dialogue synthesis model designed for expressive, multi-party conversational speech across multiple languages. With enhanced long-context modeling, MOSS-TTSD generates long-form spoken conversations from dialogue scripts with explicit speaker tags, supporting up to 60 minutes of single-pass synthesis, multi-party dialogue with up to 5 speakers, and zero-shot voice cloning from a short reference audio clip. The model supports various mainstream languages, including English and Chinese, and is adapted to several long-form scenarios. Additionally, to address limitations of existing evaluation methods, we propose TTSD-eval, an objective evaluation framework based on forced alignment that measures speaker attribution accuracy and speaker similarity without relying on speaker diarization tools. Both objective and subjective evaluation results show that MOSS-TTSD surpasses strong open-source and proprietary baselines in dialogue synthesis.

Related papers

• Covo-Audio Technical Report [61.09708870154148]
  Covo-Audio, a 7B-end LALM, directly processes continuous audio inputs and generates audio outputs within a single unified architecture.<n>Covo-Audio-Chat, a dialogue-oriented variant, demonstrates semantic strong spoken conversational abilities.
  arXiv  Detail & Related papers  (2026-02-10T14:31:11Z)
• RealTalk-CN: A Realistic Chinese Speech-Text Dialogue Benchmark With Cross-Modal Interaction Analysis [15.473595594666751]
  We introduce RealTalk-CN, the first Chinese multi-turn, multi-domain speech-text dual-modal TOD dataset.<n>RealTalk-CN captures diverse dialogue scenarios with annotated spontaneous speech disfluencies.<n>We propose a novel cross-modal chat task that authentically simulates real-world user interactions.
  arXiv  Detail & Related papers  (2025-08-06T13:12:57Z)
• Aligning Spoken Dialogue Models from User Interactions [55.192134724622235]
  We propose a novel preference alignment framework to improve spoken dialogue models on realtime conversations from user interactions.<n>We create a dataset of more than 150,000 preference pairs from raw multi-turn speech conversations annotated with AI feedback.<n>Our findings shed light on the importance of a well-calibrated balance among various dynamics, crucial for natural real-time speech dialogue systems.
  arXiv  Detail & Related papers  (2025-06-26T16:45:20Z)
• FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation [10.73307957038715]
  FMSD-TTS is a few-shot, multi-speaker, multi-dialect text-to-speech framework.<n>It synthesizes parallel dialectal speech from limited reference audio and explicit dialect labels.
  arXiv  Detail & Related papers  (2025-05-20T13:35:55Z)
• WavChat: A Survey of Spoken Dialogue Models [66.82775211793547]
  Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems.
  arXiv  Detail & Related papers  (2024-11-15T04:16:45Z)
• 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)
• Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model [8.180382743037082]
  This paper explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously.
  arXiv  Detail & Related papers  (2023-09-20T01:48:27Z)
• SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents [70.08842857515141]
  SpokenWOZ is a large-scale speech-text dataset for spoken TOD.<n>Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
  arXiv  Detail & Related papers  (2023-05-22T13:47:51Z)
• 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.