Speech emotion identifying method based on supporting vector machine

A support vector machine, emotion technology, applied in speech recognition, speech analysis, instruments, etc., can solve the problems of increased learning time, high computational complexity, and insufficient recognition rate.

Inactive Publication Date: 2007-06-06
邹采荣
View PDF0 Cites 56 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the past literature, these gender differences in acoustic parameters without considering emotional factors have been fully studied. However, there are few literatures that specifically study the influence of characteristic parameters of different genders on emotional states.
Among the various existing recognition methods, specifically, the vector segmentation type Mahalanobis distance judgment method and the principal component analysis method are too simple to obtain a good recognition rate; although the neural network method has a high degree of nonlinearity and strong Classification ability, b

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Speech emotion identifying method based on supporting vector machine
  • Speech emotion identifying method based on supporting vector machine
  • Speech emotion identifying method based on supporting vector machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0058] The technical solutions of the present invention will be further described below in conjunction with the drawings and embodiments.

[0059] As shown in Figure 1, it is a block diagram of the speech emotion recognition system, which is mainly divided into three major blocks: feature extraction and analysis module, SVM training module and SVM recognition module. The whole system execution process can be divided into training process and recognition process. The training process includes feature extraction analysis and SVM training; the identification process includes feature extraction analysis and SVM identification.

[0060] 1. Feature extraction analysis module

[0061] 1. Global structural feature parameter selection and gender regularization

[0062] The global structural feature parameters include: sentence pronunciation duration, speech rate, average pitch frequency, highest pitch frequency, average change rate of pitch frequency, average amplitude, dynamic range...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

A method for identifying voice emotion based on support vector computer includes characteristic picking up-analyzing to collate characteristic parameter selection of global structure and sex, as well as to collate characteristic parameter selection of time sequence structure and to collate sex and vowel number; support vector computer training to carry out identification on five emotions of happy, angry, sad, fear and surprise.

Description

technical field [0001] The invention relates to a speech recognition method, in particular to a speech emotion recognition system and method. Background technique [0002] The automatic speech emotion recognition technology mainly includes two problems: one is to use the characteristics in the speech signal as emotion recognition, that is, the problem of emotional feature extraction, and the other is how to classify specific speech data, that is, the problem of pattern recognition . [0003] Before the present invention, the main emotional features currently used are prosodic features and their derived parameters, such as duration, speech rate, amplitude, pitch frequency, formant, etc. Among them, the pitch frequency and the formant frequency are important emotional parameters, but due to the individual differences between people (variability of the vocal tract, vocal tract characteristics, word pronunciation pitch, etc.), it is difficult to realize the pitch It is difficu...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G10L15/00G10L15/02G10L15/06G10L15/08G10L15/28
Inventor 赵力王治平赵艳郑文明
Owner 邹采荣
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap