Speech emotion recognizing method based on hidden Markov model (HMM) / self-organizing feature map neural network (SOFMNN) hybrid model

Abstract

The invention relates to a speech emotion recognizing method based on a hidden Markov model (HMM) / self-organizing feature map neural network (SOFMNN) hybrid model. The speech emotion is recognized by combining an HMM model and an SOFMNN model through the method. The speech emotion recognizing method specifically comprises the following steps of: 1) establishing an emotion speech data base; 2) carrying out speech signal pretreatments including weighting treatment, denoising and framing windowing; 3) extracting the speech emotion characteristics including the extraction of time, energy, amplitude, fundamental frequency and formant of speech emotion signals; and 4) training and recognizing by using the HMM / SOFMNN hybrid model. Compared with the prior art, the invention overcomes the problem that the HMM is difficult to solve the problem of mutual overlapping among model categories by self and makes up the shortage in the aspect of obtaining timing information by SOFMNN, so that the speech emotion recognizing rate is improved.

Description

technical field [0001] The invention relates to a speech emotion recognition method, in particular to a speech emotion recognition method based on an HMM / SOFMNN hybrid model. Background technique [0002] Human speech signals contain rich emotional information, and identifying human emotions through speech signal analysis is a very active research topic at present. Speech emotion recognition is to recognize the speaker's emotional information from the speech signal, such as "happy, angry, sad, happy" and so on. Speech emotion recognition has broad application prospects in natural human-computer interaction and automatic supervision of security systems. [0003] Speech emotion recognition is a pattern recognition problem, and most pattern recognition and classification methods have been tried for automatic recognition of emotion in speech. Hidden Markov Model (HMM), as an ideal statistical model of speech signals, has been widely used in the field of speech processing, and ...

AI Technical Summary

Problems solved by technology

[0004] However, the HMM method has the disadvantages of requiring prior statistical knowledge of speech signals and weak classification decision-making ability. Because only the intra-class changes of features are considered, and the overlap between classes is ignored, the class judgment is only made based on the maximum value of each cumulative probability. , while ignoring the similarity between each mode, thus affecting the recognition performance of the system, its adaptive ability and robustness are not ideal

Images