DNASpeech: A Contextualized and Situated Text-to-Speech Dataset with Dialogues, Narratives and Actions
Keywords: Text-to-Speech, Voice Generation, Prompt
TL;DR: Build a dataset for prompt-based TTS task with paired situated prompt and speech extracted from movies.
Abstract: In this paper, we propose contextualized and situated text-to-speech (CS-TTS), a novel TTS task to promote more accurate and customized speech generation using prompts with Dialogues, Narratives, and Actions (DNA). While prompt-based TTS methods facilitate controllable speech generation, existing TTS datasets lack situated descriptive prompts aligned with speech data. To address this data scarcity, we develop an automatic annotation pipeline enabling multifaceted alignment among speech clips, content text, and their respective descriptions. Based on this pipeline, we present DNASpeech, a novel CS-TTS dataset with high-quality speeches with DNA prompt annotations. DNASpeech contains 2,395 distinct characters, 4,452 scenes, and 22,975 dialogue utterances, along with over 18 hours of high-quality speech recordings. To accommodate more specific task scenarios, we establish a leaderboard featuring two new subtasks for evaluation: CS-TTS with narratives and CS-TTS with dialogues. We also design an intuitive baseline model for comparison with existing state-of-the-art TTS methods on our leaderboard. Comprehensive experimental results demonstrate the quality and effectiveness of \dataname, validating its potential to drive advancements in the TTS field.
Primary Area: datasets and benchmarks
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Submission Number: 14130
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