An I-CNN Based Speech Classification Algorithm for Custom Service

Speech classification methods mainly focus on the content of the voice segment. To help better underestand the information in a segmented voice, the contents of other segments in the same paragraph should also be paid attention to. In our custom service speech classification problem, we are facing a problem of classification a series of voice segments in a conversation separately into category "custom" or "custom service". Sometimes the voice of both parties in the same conversation can be both sound like a "custom service" or both sound like "custom". In order to make the right prediction, the model needs to know not only the content of the voice segment that it's classifying, but both parties' voice in a conversation, the extra information can help the model to determine who is "more likely" to be a custom service in a conversation. We propose a method called I-CNN, which combines the info-feed layer with CNN. The Info-feed layer allows the CNN to use information from other samples in the same batch, which is helpful in improving the model's performance in our custom service speech classification problem.