Speech recognition and intelligent translation under multimodal human–computer interaction system

Abstract

Abstract The traditional translation robot is limited to the translation of single-mode text images and text videos, which has the problem of low translation accuracy. Therefore, speech recognition and intelligent translation in multimodal human–computer interaction (HCI) system are proposed. First, the network structure of speech recognition model in multi-channel HCI system is established, and the multi-head self-attention mechanism is constructed. Then, the artificial intelligence voice wake-up function is designed, and a multimodal machine translation model is constructed. On this basis, selective attention is added to obtain visual recognition of perceived text, and the decoder is used for multimodal gating fusion to realize the output of encoder translation results. Experimental results show that this method has high BLUE value and high translation accuracy.