DIS-NV feature-based emotion recognition method

A DIS-NV and emotion recognition technology, which is applied in speech recognition, speech analysis, instruments, etc., can solve the problems of difficult to effectively control feature dependencies, affect the accuracy of emotion recognition models, and take a long time to train, so as to enhance sequence processing and improve Emotion recognition effect, effect of simple training process

Inactive Publication Date: 2017-11-03
HUNAN UNIV
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AI Technical Summary

Problems solved by technology

The number of LLD features is very large, basically between 1000 and 2000. On the one hand, the training of emotion recognition models for a large number of feature values ​​is difficult and requires a long training time, making the recognition efficiency low; on

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  • DIS-NV feature-based emotion recognition method
  • DIS-NV feature-based emotion recognition method

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Embodiment D

[0037] The DIS-NV feature set of the present embodiment also includes filler words (such as: like, I see), stop words (such as: Err, Hmm) etc., certainly DIS feature words also can adopt above-mentioned non-linguistic insertion class word, language according to actual demand Insert any one or more combinations of word-like words and language-repetitive words, and other words frequently used in daily life can be used as emotional words with unfluent characteristics according to actual needs to further improve the recognition effect.

[0038] Breathing and laughter contain the effective emotional information of the speaker. The NV feature class in this embodiment specifically includes two types of utterances: breathing and laughter. It can also be set as one of them according to actual needs, or other types of utterances can be considered type to further improve the recognition effect. Breathing and laughing can use the tag words corresponding to breathing and laughing provided ...

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Abstract

The invention discloses a DIS-NV feature-based emotion recognition method. The method comprises the following steps of S1, selecting an emotion word having the non-fluency feature as a DIS feature word, selecting a sound production type having the non-language feature as an NV feature class, and constructing a DIS-NV feature set; S2, obtaining training voice texts, respectively matching the training voice texts with the DIS-NV feature set, extracting corresponding DIS-NV feature values, training by adopting a BLSTM model and obtaining a BLSTM classification model; S3, obtaining a to-be-recognized voice text, matching the to-be-recognized voice text with the DIS-NV feature set, extracting a corresponding DIS-NV feature value, recognizing by using the BLSTM classification model and outputting an emotion recognition result. According to the invention, the emotion recognition is realized through fully utilizing voice texts having the non-fluency feature and the non-language feature. The method is simple in implementation, high in recognition efficiency and high in precision.

Description

technical field [0001] The invention relates to the technical field of automatic emotion recognition, in particular to an emotion recognition method based on DIS-NV features. Background technique [0002] Emotion recognition is to identify the emotional information in the dialogue to judge the emotional state of the speaker. Through the automatic recognition of emotion, better human-computer interaction can be achieved, such as human-computer communication, conversational agency, etc. At present, emotion recognition is usually based on a category of processing methods, that is, there are several basic and common emotions in the brain, such as: happiness, sadness, surprise, fear, anger, and disgust, but the speaker Emotional states are usually complex, and a single emotional expression or limited discrete categories are usually difficult to properly describe the complex emotional state. One solution is to replace the classification with continuous emotional labels in a multid...

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Application Information

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IPC IPC(8): G10L25/63G10L15/06G10L15/14G10L15/08G10L15/183
CPCG10L15/063G10L15/08G10L15/14G10L15/183G10L25/63
Inventor 赵欢周晓晓肖宇锋陈佐
Owner HUNAN UNIV
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