Audio-visual Keyword Spotting for Mandarin Based on Discriminative Local Spatial-Temporal Descriptors

Although keyword spotting (KWS) technologies have been successfully applied to some applications, most KWS systems have a common problem of noise-robustness when applied to real-world environments. Audio-visual keyword spotting (AVKWS) using both acoustic and visual information is a solution to complementarily solve the problem. Most existing audio-visual speech recognition (AVSR) systems extract geometric features as visual features, which heavily rely on accurate and reliable detection and tracking of facial feature points. To avoid this defect of geometric features, an appearance-based discriminative local spatial-temporal descriptor (disCLBP-TOP) is proposed in this paper, which devotes to extracting robust and discriminative patterns of interest. Besides, a parallel two-step recognition based on both acoustic and visual keyword searching and re-scoring is conducted, which complementarily makes the best of two modalities under different noisy conditions. Adaptive weights for decision fusion are generated using a sigmoid function based on reliabilities of the two modalities, capable of adapting to various noisy conditions. Experiments show that our proposed parallel AVKWS strategy based on decision fusion significantly improves the noise robustness and attains better performance than feature fusion based audio-visual spotter. Additionally, disCLBP-TOP shows more competitive performance than CLBP-TOP.