Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

LipSody: Lip-to-Speech Synthesis with Enhanced Prosody Consistency

Jaejun Lee, Yoori Oh, Kyogu Lee

TL;DR

LipSody tackles the challenge of producing expressive, speaker-consistent speech from silent video. It builds on LipVoicer by explicitly modeling prosody (pitch $\boldsymbol{p}$ and energy $\boldsymbol{e}$) using three visual cues: $\boldsymbol{s}$, $\boldsymbol{c}$, and $\boldsymbol{o}$, plus an independent prosody predictor. The results show LipSody improves prosody metrics ($GF_0$, $LF_0$, $EC$, $Resem$) while maintaining $WER$ comparable to the baseline, and analysis reveals emotion cues and oracle prosody can further boost performance. This work highlights the interplay between prosody and intelligibility in lip-to-speech and suggests directions for more natural, personalized speech synthesis from visual input.

Abstract

Lip-to-speech synthesis aims to generate speech audio directly from silent facial video by reconstructing linguistic content from lip movements, providing valuable applications in situations where audio signals are unavailable or degraded. While recent diffusion-based models such as LipVoicer have demonstrated impressive performance in reconstructing linguistic content, they often lack prosodic consistency. In this work, we propose LipSody, a lip-to-speech framework enhanced for prosody consistency. LipSody introduces a prosody-guiding strategy that leverages three complementary cues: speaker identity extracted from facial images, linguistic content derived from lip movements, and emotional context inferred from face video. Experimental results demonstrate that LipSody substantially improves prosody-related metrics, including global and local pitch deviations, energy consistency, and speaker similarity, compared to prior approaches.

LipSody: Lip-to-Speech Synthesis with Enhanced Prosody Consistency

TL;DR

LipSody tackles the challenge of producing expressive, speaker-consistent speech from silent video. It builds on LipVoicer by explicitly modeling prosody (pitch and energy ) using three visual cues: , , and , plus an independent prosody predictor. The results show LipSody improves prosody metrics (, , , ) while maintaining comparable to the baseline, and analysis reveals emotion cues and oracle prosody can further boost performance. This work highlights the interplay between prosody and intelligibility in lip-to-speech and suggests directions for more natural, personalized speech synthesis from visual input.

Abstract

Lip-to-speech synthesis aims to generate speech audio directly from silent facial video by reconstructing linguistic content from lip movements, providing valuable applications in situations where audio signals are unavailable or degraded. While recent diffusion-based models such as LipVoicer have demonstrated impressive performance in reconstructing linguistic content, they often lack prosodic consistency. In this work, we propose LipSody, a lip-to-speech framework enhanced for prosody consistency. LipSody introduces a prosody-guiding strategy that leverages three complementary cues: speaker identity extracted from facial images, linguistic content derived from lip movements, and emotional context inferred from face video. Experimental results demonstrate that LipSody substantially improves prosody-related metrics, including global and local pitch deviations, energy consistency, and speaker similarity, compared to prior approaches.
Paper Structure (23 sections, 1 equation, 1 figure, 4 tables)

This paper contains 23 sections, 1 equation, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Lip-to-speech
  4. Methods
  5. Diffusion-based lip-to-speech network
  6. Enhancing prosody consistency with LipSody
  7. Prosody prediction
  8. Model architecture
  9. Experiments
  10. Datasets
  11. Comparison systems
  12. Implementation details
  13. Metrics
  14. Objective metrics (Conventional)
  15. Objective metrics (Prosody-related)
  16. ...and 8 more sections

Figures (1)

  • Figure 1: Overview of LipSody, the proposed diffusion-based lip-to-speech framework with enhanced pitch consistency. During training, ground-truth pitch and energy values are used to provide prosody-related supervision to the lip-to-speech network. For inference, an independent network is trained to predict these prosody features from lip movement-based linguistic content, face image-based speaker identity, and face video-based emotional expression.