Speech Emotion Recognition Research Based On Hilbert-Huang Transformation

Speech emotion recognition demands distilling emotional features from speech signals and adopting certain pattern recognition method to determine which emotion the speech contains. It is a new area in speech processing and has wide applications. Feature extraction, which reflects the results directly , is the most important factor.In this thesis, Hilbert-Huang Transformation is applied to emotional speech processing and analyzing. HHT features are distilled and text-independent, speaker-independent emotion recognition is simulated. The details are as follows:Firstly, theory of HHT is discussed, its essence and merit in signal processing is shown, based on which, marginal energy is proposed, and used in emotional speech analysis together with marginal spectrum. Statistical analysis of four emotions: happy, angry, boring and netrual demonstrates that marginal energy and marginal spectrum well reflect the energy distribution characteristics in time and frequency domain respectively. Thus, they can be a basis of emotion recognition. Then statistical Hilbert energy (EHHT) is distilled from marginal energy, sub-band energy(SE) and its derivation(DSE), sub-band energy cepstrum coefficients(SECC) and its derivation (DSECC) are distilled from marginal spectrum. At last, with pattern recognition theory Vector Quantization(VQ), speaker-independent and text-independent emotion recognition is simulated using the above features respectively. Results demonstrate that, time-domain feature or frequency-domain feature respectively can not recognize speech emotion effectively, but combination of these two features make a good recognition rate of 98.53%.In this thesis, HHT is applied to speech processing and emotion recognition, the use of HHT time-frequency features not only enhance the recognition rate, but also reduce the code size, thus , the research of this thesis is both meaningful and feasible .