Characteristics of human auditory model based on compensation of glottal features in speech emotion recognition

The speech signal carries emotional message during its production. With the analysis on relation between sound production and glottis, the paper has introduced the glottal features into speech emotion recognition, proposed the model where the glottis is used for compensation of glottal features, and extracted the feature of Glottal Compensation to Zero Crossings with Maximal Teager Energy Operator (GCZCMT). Two experiments have been designed, including that: firstly, the single emotional speech databases of TYUT and Berlin are respectively used for experiment (the purpose of such experiment is to research the emotion recognition capability of GCZCMT feature, and the experimental results show that GCZCMT feature is a feature possibly and effectively distinguishing emotional state); secondly, this experiment is one of mixing speech database (the purpose of such experiment is to research the emotion recognition capability of GCZCMT feature on ross-database language, and the experimental results show that the database dependency of GCZCMT feature is the minimum, and such feature is more suitable for actual complex language environment, and has the higher practical value.