dots.tts.edit: Precisely Controlled Speech Editing with a Continuous Autoregressive Model

2026-08-02

AI Summary

This work addresses the challenge of imprecise control in existing speech editing methods under natural language instructions, which often suffer from semantic ambiguity in specifying edit types, parameters, and target regions. To overcome this, the authors propose a structured editing interface grounded in transcribed text, employing XML-style tags to explicitly denote operation types and anchor them to specific transcript spans or boundaries, thereby constructing a semantic timeline that circumvents the need for explicit time alignment. Building upon this framework, they enhance the continuous autoregressive model dots.tts to support four composable editing dimensions—lexical content, emotion, prosody, and pauses—while preserving contextual integrity in unedited segments. The contributions include the first structured instruction framework for speech editing, a task-oriented data curation pipeline, and doteBench, the first bilingual benchmark for precise evaluation. Experiments demonstrate state-of-the-art instruction-following accuracy and local fidelity across five editing tasks in doteBench, with audio quality comparable to leading open-source systems and no significant degradation in zero-shot TTS error rates or speaker similarity relative to the base model.

Abstract

Speech editing for content creation requires precise control over both what an edit should do and where it should apply. Free-form natural language provides a flexible interface for expressing edit requests, but its ambiguity may leave the intended operation, parameters, or target region underspecified. We study a precise and explicit interface for speech editing: a transcript-grounded structural edit instruction with XML-style tags explicitly specifies typed operations and localizes them to transcript spans or boundaries. This semantic timeline avoids explicit timestamp alignment and provides an externally inspectable contract for compositional edits. We instantiate the interface in dots.tts.edit, an editor adapted from the continuous autoregressive dots.tts foundation model. Four representative speech-creation controls cover lexical content, affective expression, pitch and speaking-rate delivery, and temporal phrasing through text, emotion, prosody, and pause editing. Task-specific data pipelines construct operation- and scope-controlled pairs while retaining source-derived context outside each target region. We further introduce doteBench, a bilingual evaluation suite that measures precise instruction following, local preservation, and audio quality across the four controls and their composition. Experiments show leading overall instruction following and local preservation across its five editing categories, while audio quality remains comparable to existing open-source systems. Across three Seed-TTS-Eval shards, the model shows negligible differences from the base model in zero-shot TTS recognition error rate and speaker similarity. The code and model will be released soon.

Problem

• speech editing
• precise control
• edit instruction
• content creation
• ambiguity

Innovation

• structured speech editing
• transcript-grounded editing
• continuous autoregressive model
• semantic timeline
• doteBench