口音是语音识别面临的主要挑战之一，为解决多口音语音识别问题，进一步探索口音识别与语音识别任务之间的相互作用，该文提出一种基于尖峰特征的口音识别和语音识别多任务学习方法。该方法首先通过共享部分编码器底层网络，利用口音信息隐式增强特定口音的声学特征，提升口音语音识别性能；其次，通过分析口音特征可发现，口音特征大部分由空白帧组成，而更能反映口音差异的有效标签帧未发挥主要作用。因此，该文在多任务学习框架下，将有效标签对应的连接主义时序分类(connectionist temporal classification，CTC)尖峰特征作为口音识别的输入特征，从而在一定程度上提升口音识别性能。在英文Common Voice和AESRC2020数据集上的试验结果表明：与直接混合所有数据训练的模型相比，该文方法在语音识别性能上分别绝对提升0.6%和1.0%；在口音识别上，基于CTC尖峰特征的口音识别性能分别绝对提升0.7%和1.9%。

Objective: Accent is a significant challenge in speech recognition. To address the problem of multi accent speech recognition and further explore the interaction between accent and speech recognition tasks, this paper proposes a multi-task learning method for accent and speech recognition based on spike features. The multi-task learning approach can simultaneously model both accent recognition and speech recognition, simplifying system complexity and making the model more compact. In addition, the information shared between tasks can complement each other, improving the performance of each task. However, these methods also have several limitations. First, there should be more exploration and analysis of the accent information captured by different encoder layers and its effect on speech recognition performance. Second, the interaction between the two tasks has yet to be further explored, such as using speech recognition information to assist in accent recognition. Therefore, this paper will conduct further research on the aforementioned issues. Methods: In this paper, a multi-task learning framework for accent and speech recognition is constructed within the ESPnet environment. By sharing parts of the encoder's underlying network, the accent information is used to implicitly enhance the acoustic features of a specific accent to improve the performance of accent and speech recognition. This paper analyzes the encoder's hidden layer features to further investigate the interaction between the two tasks. The results show that the accent features are mainly composed of blank frames, whereas the effective label frames, which better reflect accent differences, do not play a significant role. To address this problem, this paper proposes using connectionist temporal classification (CTC) spike features corresponding to valid labels as the input features for accent recognition within a multi-task learning framework to improve accent recognition performance. During forward propagation in model training, CTC pseudo-label alignment information is calculated, and the corresponding hidden layer encoding features are obtained through the indices of non-blank frames. These features are then subjected to statistical pooling, and the resulting features are used for accent recognition training. All the parameters are updated synchronously during the joint training process. Given that speech sequences are typically much longer than text sequences, experiments are conducted using Spike-Frame and Spike-Chunk features as the accent features, respectively. The Spike-Chunk refers to extending a certain number of frames on both sides of the Spike-Frame index. Results: Experiments conducted on English Common Voice and AESRC2020 datasets demonstrated that the proposed method improved speech recognition performance by absolutely 0.6% and 1.0%, respectively, compared with the model trained with all the data mixed directly. In accent recognition, the performance based on CTC spike features improved by absolutely 0.7% and 1.9%, respectively. Conclusions: This paper proposes a model for both accent recognition and speech recognition within a multi-task learning framework. The experiments show that the accent information can enhance the hidden layer features of the encoder to some extent, thereby improving the speech recognition performance. Meanwhile, the spike features of valid labels in speech recognition also enhance the accent recognition performance to some degree. This study aims to further explore the interaction between accent and speech recognition tasks to improve the performance of both tasks and achieve the expected results.