# Lipper: Synthesizing Thy Speech using Multi-View Lipreading

## TL;DR

Lipper is a multi-view lipreading system that synthesizes speech from silent videos, improving accuracy over single-view methods and demonstrating real-time performance and user comprehensibility.

## Contribution

This work introduces Lipper, a novel multi-view lipreading to speech synthesis system modeled as a regression task, moving beyond traditional text classification approaches.

## Key findings

- Multi-view videos improve speech reconstruction accuracy.
- Lipper achieves real-time speech synthesis.
- User study confirms audio comprehensibility.

## Abstract

Lipreading has a lot of potential applications such as in the domain of surveillance and video conferencing. Despite this, most of the work in building lipreading systems has been limited to classifying silent videos into classes representing text phrases. However, there are multiple problems associated with making lipreading a text-based classification task like its dependence on a particular language and vocabulary mapping. Thus, in this paper we propose a multi-view lipreading to audio system, namely Lipper, which models it as a regression task. The model takes silent videos as input and produces speech as the output. With multi-view silent videos, we observe an improvement over single-view speech reconstruction results. We show this by presenting an exhaustive set of experiments for speaker-dependent, out-of-vocabulary and speaker-independent settings. Further, we compare the delay values of Lipper with other speechreading systems in order to show the real-time nature of audio produced. We also perform a user study for the audios produced in order to understand the level of comprehensibility of audios produced using Lipper.