Chinese Speech Recognition Using Conformer Fused with Max Pooling

Speech recognition technology enables machines to understand human speech using advanced algorithms and signal processing technologies, thereby making communication between humans and machines more convenient. Most existing studies on end-to-end speech recognition focus on optimizing the Conformer model. The Conformer encoder suffers from the issue of insufficient extraction of fine-grained local speech features. To resolve these issues, this study proposes a Chinese speech recognition method based on Max Pooling (MP). First, the output of the gated linear unit in the convolutional module of the encoder is max-pooled along the time dimension to extract fine-grained local features corresponding to the characteristics of multiple speech signal frames. Second, these pooled features are fused with the coarse-grained local features extracted via Depthwise Convolution (DWC) using the element-wise sum method to increase the amount of information on local speech features and improve the speech recognition accuracy of the Conformer model. The experimental results on the public Chinese dataset Aishell-1 show that the improved model can reduce the Character Error Rate (CER) of the baseline model from 5.58% to 5.32% and from 5.06% to 4.92% by decoding using greedy search and attention rescoring, respectively.