Speaker Recognition using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions

With the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices arc providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are not suitable models to run on smart home devices. In this paper, we compare relatively small Convolutional Neural Networks (CNN) and evaluate effectiveness of speaker recognition using these models on edge devices. In addition, we also apply transfer learning technique to deal with a problem of limited training data. By developing solution suitable for running inference locally on edge devices, we eliminate the well-known cloud computing issues, such as data privacy and network latency, etc. The preliminary results proved that the chosen model adapts the benefit of computer vision task by using CNN and spectrograms to perform speaker classification with precision and recall similar to 84% in time less than 60 ms on mobile device with Atom Cherry Trail processor.