Multi-mode non-contact emotion analyzing and recording system

Abstract

The invention discloses a multi-mode non-contact emotion analyzing and recording system. The system is characterized by being composed of a voice receiving module, a voice feature extracting and processing module, a speech recognition module, a textural feature extracting and processing module, a comprehensive scheduling module, a displaying module and a clock module; the voice receiving module is used for completing receiving of voice from outside environment, the voice feature extracting and processing module is used for acquiring voice frequency emotion labeling information of speech, the voice recognition module is used for completing conversion from speech content to textural content, the textural feature extracting and processing module is used for acquiring textural emotion labeling information of the speech, the comprehensive scheduling module is used for completing processing, storing and scheduling of all data, the displaying module is used for completing displaying of detected speech emotion state, and the clock module is used for completing time recording and providing a time labeling function. By the multi-mode non-contact emotion analyzing and recording system, a textural mode and a voice frequency mode can be integrated to recognize speech emotions, so that accuracy of recognition is improved.

Description

technical field [0001] The invention relates to the field of human-computer emotion interaction, in particular to a multimodal non-contact emotion analysis and recording system. Background technique [0002] Language is the most important tool for communication between people. Human speech includes text symbol information and also contains people's emotions. Artificial processing of emotional information features from speech is of great significance in the field of artificial intelligence. Humans communicate through language, and human emotions are expressed through multiple channels and modes, such as expressing emotions through language content, audio, facial expressions, and body movements. Speech emotion recognition is to identify the speaker's emotional information from voice signals. [0003] The current research mainly extracts emotional feature information from speech prosody. The speech emotion recognition system mainly relies on the low-level acoustic features of s...

AI Technical Summary

Problems solved by technology

[0003] The current research mainly extracts emotional feature information from speech prosody. The speech emotion recognition system mainly relies on the low-level acoustic features of speech for recognition. The representative features are pitch frequency, formant, short-term average zero-crossing rate and pronunciation duration. Time, etc., this method tends to lead to a higher feature dimension. Pattern recognition research shows that the accuracy rate is not proportional to the dimension of the feature space, and the generalization ability will be weakened in high-dimensional situations, and even lead to a higher dimensionality. disaster

[0004] There is also a linguistics approach that considers the emotional analysis of speech signals, considers the semantic components of the speech text, and uses the semantics and grammar of sentences to provide emotional clues to the speaker. History, word frequency, etc.; the disadvantage of this method is that it requires a lot of knowledge, which first brings difficulties to speech recognition, and semantic analysis requires relevant language knowledge, which increases the difficulty of sentiment analysis. The method is complex and difficult to implement at this stage

[0005] In the field of speech emotion information processing, almost all pattern recognition methods are used, such as artificial neural network (ANN), hidden Markov model (HMM), mixed Gaussian model (GMM), support vector machine (SVM), etc., but if the Comparing all these results together, it can be found that the means of feature extraction are extremely limited. Almost all studies use prosodic features or the linear combination and transformation of these prosodic features as the research object, and most of them are only in the audio mode. Analysis, so that the speech emotional features are always limited to a smaller category, not comprehensive enough

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