mlsanityData to 14 Oct 2026

SpeakEasy: Enhancing Text-to-Speech Interactions for Expressive Content Creation

arXiv 2504.05106 ↗First posted 7 Apr 2025cs.HC cs.AI cs.LGcited by 1

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Abstract (arXiv)

Novice content creators often invest significant time recording expressive speech for social media videos. While recent advancements in text-to-speech (TTS) technology can generate highly realistic speech in various languages and accents, many struggle with unintuitive or overly granular TTS interfaces. We propose simplifying TTS generation by allowing users to specify high-level context alongside their script. Our Wizard-of-Oz system, SpeakEasy, leverages user-provided context to inform and influence TTS output, enabling iterative refinement with high-level feedback. This approach was informed by two 8-subject formative studies: one examining content creators' experiences with TTS, and the other drawing on effective strategies from voice actors. Our evaluation shows that participants using SpeakEasy were more successful in generating performances matching their personal standards, without requiring significantly more effort than leading industry interfaces.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 7 Apr 2025.

Builds on

14 of 32 references.

References · 14

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2 more
  1. 2024Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Modelscited by 73
  2. 2024Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modelingcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026YODAS v3: Over 1 Million Hours of High-Bandwidth, Stereophonic, Multilingual Speechcited by 0