唇语识别是将单个说话人嘴唇运动的无声视频翻译成文字的一种技术.由于嘴唇运动幅度较小,现有唇语识别方法的特征区分能力和泛化能力都较差.针对该问题,文中分别从时间、空间和通道三个维度研究唇语视觉特征的提纯问题,提出基于多重视觉注意力的唇语识别方法(Lipreading Based on Multiple Visual Attention Network,LipMVA).首先利用通道注意力自适应校准通道级别的特征,减轻无意义通道的干扰.然后使用两种粒度不同的时空注意力,抑制不重要的像素或帧的影响.CMLR、GRID数据集上的实验表明LipMVA可降低识别错误率,由此验证方法的有效性.
Abstract
Lipreading is a technology that translates the silent video of a single speaker's lip motion into text.Due to the small amplitude of lip movements,the feature differentiation ability and the generalization ability of the model are both weak.To address this issue,the purification of lipreading visual features is studied from three dimensions including time,space and channel.A method for lipreading based on multiple visual attention network(LipMVA)is proposed.Firstly,channel-level features are calibrated adaptively by channel attention to mitigate the interference from meaningless channels.Then,two spatio-temporal attention modules with different granularities are employed to suppress the effect of unimportant pixels or frames.Finally,experiments on CMLR and GRID datasets demonstrate LipMVA can reduce the error rate and therefore its effectiveness is verified.
维普
万方数据