Purrai: A Deep Neural Network based Approach to Interpret Domestic Cat Language

Abstract

Being able to understand and communicate with domestic cats has always been fascinating to humans, although it is considered a difficult task even for phonetics experts. In this paper, we present our approach to this problem: Purrai, a neural-network-based machine learning platform to interpret cat’s language. Our framework consists of two parts. First, we build a comprehensively constructed cat voice dataset that is 3.7x larger than any existing public available dataset [1]. To improve accuracy, we also use several techniques to ensure labeling quality, including rule-based labeling, cross validation, cosine distance, and outlier detection, etc. Second, we design a two-stage neural network structure to interpret what cats express in the context of multiple sounds called sentences. The first stage is a modification of Google’s Vggish architecture [2] [3], which is a Convolutional Neural Network (CNN) architecture that focuses on the classification of nine primary cat sounds. The second stage takes the probability outputs of a sequence of sound classifications from the first stage and determines the emotional meaning of a cat sentence. Our first stage architecture generates a top-l and top-2 accuracy of 74.1% and 92.1%, better than that of the state-of-the-art approach: 64.9% and 83.4% [4]. Our sentence-based AI model achieves an accuracy of 81.1% for emotion prediction.