// radar de ia

Áudio & Voz

Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.

Blog LLMs & Texto

MindAlign: Decoding Inner Speech from fMRI Signals via Multimodal Embedding Alignment under Limited Data

arXiv:2606.20696v1 Announce Type: new Abstract: Decoding inner speech from non-invasive brain signals remains a fundamental challenge due to the absence of overt linguistic output, limited training data, and large inter-subject variability. Existing brain-to-text approaches often rely on task-specific decoder fine-tuning, which restricts scalability and complicates adaptation to new participants. We propose MindAlign, a decoupled two-stage brain-to-language framework that enables open-ended text...

23.06.2026
Blog LLMs & Texto

EmoInstruct-TTS: Dual-Path Instruction-Guided Emotional Speech Synthesis

arXiv:2606.20650v1 Announce Type: new Abstract: Instruction-based controllable speech synthesis enables users to specify emotions through natural language. However, existing approaches often rely on coarse emotion labels and lack explicit modeling of fine-grained intensity. We propose EmoInstruct-TTS, a dual-path instruction-guided framework for emotional speech synthesis. We introduce Emotion2embed, a supervised semantic-acoustic emotion embedding covering 48 emotional states, including fine-gr...

23.06.2026
Blog LLMs & Texto

LLM-Based Multi-Reference Evaluation for Efficient and Robust Assessment of Phrase Break Annotations

arXiv:2606.21098v1 Announce Type: new Abstract: Reliable evaluation of phrase break annotations is crucial, as subtle variations in prosodic boundaries directly affect the clarity and naturalness of speech. However, existing approaches exhibit major limitations: single-reference evaluation assumes a unique gold phrasing for an utterance despite multiple valid phrasings, while human judgment, though flexible, is labor-intensive and unscalable. To address these, we propose LLM-based Multi-Referenc...

23.06.2026
1 / 3 próxima →
27 itens no radar