·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains1h◆DuckDuckGo makes its ‘no-AI’ search engine easier to access as its traffic booms2h◆Microsoft to unveil new AI models and Windows improvements at Build2h◆AI is blowing up music. How should the Grammys handle it?2h◆Strava blames zero-code AI apps and scrapers as it tightens API access2h◆Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic2h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action12h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI12h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability12h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents12h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning12h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI12h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes12h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies12h◆Universal Decision Learners12h◆Algorithmic Recourse of In-Context Learning for Tabular Data12h◆Graph Machine Learning in the Era of Large Language Models (LLMs)12h◆NGDBench: Towards Neural Graph Data Management12h◆End-to-End Compression for Tabular Foundation Models12h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning12h◆Introducing Mellum2: A 12B Mixture-of-Experts Model by JetBrains1h◆DuckDuckGo makes its ‘no-AI’ search engine easier to access as its traffic booms2h◆Microsoft to unveil new AI models and Windows improvements at Build2h◆AI is blowing up music. How should the Grammys handle it?2h◆Strava blames zero-code AI apps and scrapers as it tightens API access2h◆Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic2h◆Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action12h◆Physically Viable World Models: A Case for Query-Conditioned Embodied AI12h◆Discovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability12h◆Diagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents12h◆Answer-Set-Programming-based Abstractions for Reinforcement Learning12h◆TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI12h◆Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes12h◆BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies12h◆Universal Decision Learners12h◆Algorithmic Recourse of In-Context Learning for Tabular Data12h◆Graph Machine Learning in the Era of Large Language Models (LLMs)12h◆NGDBench: Towards Neural Graph Data Management12h◆End-to-End Compression for Tabular Foundation Models12h◆SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning12h◆
News/MOSS-VoiceGenerator: Create Realistic Voices with Natural Language Descriptions
arxiv
PublishedMarch 31, 2026 at 4:00 AM

MOSS-VoiceGenerator: Create Realistic Voices with Natural Language Descriptions

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2603.28086v1 Announce Type: cross Abstract: Voice design from natural language aims to generate speaker timbres directly from free-form textual descriptions, allowing users to create voices tailored to specific roles, personalities, and emotions. Such controllable voice creation benefits a wid

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivPhysically Viable World Models: A Case for Query-Conditioned Embodied AI12harxivDiscovering a Zeta Map Algorithm on Dyck Paths via Mechanistic Interpretability12harxivDiagnosing Failure Modes of Shared-State Collaboration in Resource-Constrained Visual Agents12harxivAnswer-Set-Programming-based Abstractions for Reinforcement Learning12h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews