Speech emotion recognition method for extracting depth space attention characteristics based on spectrogram

A technology of speech emotion recognition and depth extraction, which is applied in speech recognition, speech analysis, character and pattern recognition, etc., and can solve problems of potential nature (neglect of relevance, etc.)
CN109637522AActive Publication Date: 2019-04-16HANGZHOU DIANZI UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
HANGZHOU DIANZI UNIV
Publication Date
2019-04-16

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Abstract

The invention discloses a speech emotion recognition method for extracting depth space attention characteristics based on a spectrogram, which comprises the following steps : a, preprocessing speech in a standard emotion database marked with specified emotion labels to generate a spectrogram; b, sending the speech spectrogram into an AItti model to extract SEF characteristics; c, sending the speech spectrogram into a speech emotion model to extract shallow features; d, taking the SEF feature and the shallow feature as input, and sending the input to a CSWNet to generate a calibration weight feature; and e, sending the calibration weight characteristics to a network layer behind the CRNN, extracting depth emotion characteristics, and performing emotion classification through a Softmax classifier to generate a final classification result. Compared with the traditional model, the method disclosed by the invention has the advantages that the average recognition rate is improved by 8.43% onthe premise of slightly increasing the complexity of the model, the non-similar emotion distinguishing effect is obvious, and the generalization property is good.
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Description

technical field

[0001] The invention belongs to the field of artificial intelligence emotion recognition, in particular to a speech emotion recognition method for extracting deep space attention features based on spectrograms. Background technique

[0002] With the rapid development of artificial intelligence technology and the robot industry, people continue to put forward higher requirements for the interaction of artificial intelligence. However, most artificial intelligence systems so far cannot recognize and respond to various human emotions. At present, the research on human-computer interaction mainly focuses on image emotion recognition and speech emotion recognition (SpeechEmotion Recognition, SER), while image emotion recognition is mainly aimed at the recognition of human facial expressions. Speed ​​and other issues, which lead to technically accurate implementation is limited, and image emotion recognition has high hardware requirements and is difficult to be wi...

Claims

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