Multi-Grained Spatio-temporal Modeling for Lip-reading

TL;DR

This paper introduces a multi-grained spatio-temporal model for lip-reading that captures detailed and broad speech patterns, improving recognition accuracy across diverse speakers and conditions.

Contribution

It proposes a novel multi-level feature extraction and a bidirectional ConvLSTM with attention for robust lip-reading, addressing challenges of similar lip movements and speaker variability.

Findings

Effective in distinguishing words with similar phonemes

Robust to speaker and lighting variations

Outperforms existing methods on benchmark datasets

Abstract

Lip-reading aims to recognize speech content from videos via visual analysis of speakers' lip movements. This is a challenging task due to the existence of homophemes-words which involve identical or highly similar lip movements, as well as diverse lip appearances and motion patterns among the speakers. To address these challenges, we propose a novel lip-reading model which captures not only the nuance between words but also styles of different speakers, by a multi-grained spatio-temporal modeling of the speaking process. Specifically, we first extract both frame-level fine-grained features and short-term medium-grained features by the visual front-end, which are then combined to obtain discriminative representations for words with similar phonemes. Next, a bidirectional ConvLSTM augmented with temporal attention aggregates spatio-temporal information in the entire input sequence, which is expected to be able to capture the coarse-gained patterns of each word and robust to various conditions in speaker identity, lighting conditions, and so on. By making full use of the information from different levels in a unified framework, the model is not only able to distinguish words with similar pronunciations, but also becomes robust to appearance changes. We evaluate our method on two challenging word-level lip-reading benchmarks and show the effectiveness of the proposed method, which also demonstrate the above claims.