arXiv (All)
open access
Combining Masked Language Modeling and Cross-Modal Contrastive Learning for Prosody-Aware TTS
Text encoders in neural text-to-speech (TTS) see only linguistic content, so prosody must be inferred from paired data and synthesised intonation is often flat. Contrastive text-audio pretraining (CLAPSpeech) injects prosodic knowledge by contrasting acoustic realisations of the same phoneme, but it
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Taming Long-form Text-to-Speech#1123396
- Hearing's Blind Spots: Toward Perceptual Compute Allocation in Neural Text-to-Speech#1324657
- UniVoice: Unifying Autoregressive ASR and Flow-Matching based TTS with Large Language Models#927171
- PromptSep-Speaker: Generative Target Speaker Extraction via Multimodal Prompting and Semantic Alignment#1387354
- What determines the success of AI voice-cloned speech? Prosodic and acoustic evidence on three TTS systems#219484
Record · ID 1423824
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive