The Extraction Method of Emotional Feature Based on Children's Spoken Speech

Abstract

Most modern people ignore the importance of reading aloud. However, for children aged 5-12, reading aloud is not only an essential skill in the learning process, but also an effective means of cultivating sentiment. Because there is a nonlinear relationship between the characteristics of the spoken speech signal and the evaluation criteria, the emotional features suitable for children's reading evaluation are extracted from the audio signal, which is very important for the recognition of children's reading emotions. However, automatically recognizing emotions from speech is a challenging task, and its recognition depends on the validity of the speech emotion features and the accuracy of the model. In this research, we start with traditional Low Level Descriptors (LLD) to learn emotion-related features automatically which were found in speech, using High Level Statistics Functions (HSF), and emotion-related Short time frame level acoustic features can be learned. These features are appropriately aggregated into a compact feature representation in conjunction with a spectrogram to form a set of features that effectively characterize the emotion signal. The proposed solution is evaluated on the children's emotional reading speech library and shows more accurate predictions than existing emotion recognition algorithms.