Quality Inspection Of Intelligent Voice Text Based On Attention Mechanism

In recent years,with the proposal of digital logistics,express enterprises have launched new explorations and attempts in informatization,digitalization and intelligence,which will gradually realize digital transformation,drive the entire express industry to further improve quality and efficiency,and make logistics and transportation more accurate,fast,intelligent and efficient.The competition among express enterprises is no longer a low-level homogeneous competition,but a new round of competition with high-quality services as the core.In order to ensure customer satisfaction,it is necessary to control the service quality of customer service staff.Specifically,it is to inspect the communication process between customer service staff and customers.The existing quality inspection methods for intelligent voice text can generally achieve good modeling results on shorter texts,but the classification performance on long texts needs to be improved.In response to the difficulty of long text classification in intelligent voice text quality inspection tasks,this paper applies the attention mechanism to the industry of speech text quality inspection,and the main research points are as follows:(1)A quality inspection method based on the full attention mechanism is proposed.Due to the influence of computational complexity,the full attention mechanism is difficult to handle ultra-long text.This article first intercepts the front part of the ultra-long text,then extracts keywords from the entire text segment,and finally inputs the truncated text and keywords into the model to complete the quality inspection task.(2)In practical applications,a significant portion of speech text will belong to long text.In this paper,we propose another quality inspection method for handling long conversations,using a model based on sparse attention mechanism to extract semantic features of conversation text.The sparse attention mechanism reduces the spatiotemporal complexity of computation,allowing the model to input longer text sequences.(3)The results of the model quality control are presented to the user through a Java Web application system,in which different users can review,appeal,approve,and manually spot-check operations.