MA-LipNet: Multi-Dimensional Attention Networks for Robust Lipreading

TL;DR

This paper introduces MA-LipNet, a multi-attention network that enhances lipreading accuracy by refining features across temporal, spatial, and channel dimensions, demonstrating superior performance on benchmark datasets.

Contribution

The paper proposes a novel multi-attention framework with sequential attention modules for improved feature discrimination in lipreading tasks.

Findings

Reduces Character Error Rate (CER) and Word Error Rate (WER) on CMLR and GRID datasets.

Outperforms several state-of-the-art lipreading methods.

Validates the effectiveness of multi-dimensional feature refinement.

Abstract

Lipreading, the technology of decoding spoken content from silent videos of lip movements, holds significant application value in fields such as public security. However, due to the subtle nature of articulatory gestures, existing lipreading methods often suffer from limited feature discriminability and poor generalization capabilities. To address these challenges, this paper delves into the purification of visual features from temporal, spatial, and channel dimensions. We propose a novel method named Multi-Attention Lipreading Network(MA-LipNet). The core of MA-LipNet lies in its sequential application of three dedicated attention modules. Firstly, a \textit{Channel Attention (CA)} module is employed to adaptively recalibrate channel-wise features, thereby mitigating interference from less informative channels. Subsequently, two spatio-temporal attention modules with distinct granularities-\textit{Joint Spatial-Temporal Attention (JSTA)} and \textit{Separate Spatial-Temporal Attention (SSTA)}-are leveraged to suppress the influence of irrelevant pixels and video frames. The JSTA module performs a coarse-grained filtering by computing a unified weight map across the spatio-temporal dimensions, while the SSTA module conducts a more fine-grained refinement by separately modeling temporal and spatial attentions. Extensive experiments conducted on the CMLR and GRID datasets demonstrate that MA-LipNet significantly reduces the Character Error Rate (CER) and Word Error Rate (WER), validating its effectiveness and superiority over several state-of-the-art methods. Our work highlights the importance of multi-dimensional feature refinement for robust visual speech recognition.