A Weighted Discrete KNN Method for Mandarin Speech and Emotion Recognition

Speech signal is a rich source of information and convey more than spoken words, and can be divided into two main groups: linguistic and nonlinguistic. The linguistic aspects of speech include the properties of the speech signal and word sequence and deal with what is being said. The nonlinguistic properties of speech have more to do with talker attributes such as age, gender, dialect, and emotion and deal with how it is said. Cues to nonlinguistic properties can also be provided in non-speech vocalizations, such as laught or cry. The main investigated linguistic and nonlinguistic attributes in this article were those of audio-visual speech and emotion speech. In a conversation, the true meaning of the communication is transmitted not only by the linguistic content but also by how something is said, how words are emphasized and by the speaker’s emotion and attitude toward what is said. The perception of emotion in the vocal expressions of others is vital for an accurate understanding of emotional messages (Banse & Scherer, 1996). In the following, we will introduce the audio-visual speech recognition and speech emotion recognition, which are the applications of our proposed weighted discrete K-nearest-neighbor (WD-KNN) method for linguistic and nonlinguistic speech, respectively. The speech recognition consists of two main steps, the feature extraction and the recognition. In this chapter, we will introduce the methods for feature extraction in the recognition system. In the post-processing, the different classifiers and weighting schemes on KNN-based recognitions are discussed for the speech recognition. The overall structure of the proposed system for audio-visual and speech emotion recognition is depicted in Fig. 1. In the following, we will briefly introduce the previous researches on audio-visual and speech emotion recognition.