A Speech Emotion Recognition Model and Recognition Method Based on Joint Feature Representation

Abstract

The invention discloses a voice emotion recognition model and method based on joint feature representation, and relates to voice emotion recognition technology. A convolutional recurrent neural network model is improved, a hidden layer in the neural network is configured to learn the joint feature representation of a spectral depth feature and a manual feature, and the joint feature extraction andsentiment classification are integrated into an end-to-end network model. The joint feature utilizes the complementarity between the spectral depth feature and the manual feature, makes full use of the emotional information carried in the voice, and more perfectly models the voice emotion. In addition, the end-to-end network model reduces parameter redundancy due to an intermediate output layer.The voice emotion recognition method based on joint feature representation improves the recognition accuracy of the voice emotions compared with an original voice emotion recognition method based on apure convolutional recurrent neural network.

Description

technical field [0001] The invention relates to speech emotion recognition technology, in particular to a speech emotion recognition model (HSF-CRNN) construction based on joint feature representation convolutional neural network (HSF-CRNN) and a speech emotion recognition method. Background technique [0002] Emotion recognition helps to provide a humanized experience for human-computer interaction, enabling the computer to perceive and analyze the user's emotional state, and then generate a corresponding response. It is an important capability that computers must have in the future. Among them, speech is the basic way of human communication, and speech emotion recognition is particularly important. Speech emotion recognition is the process of marking the emotion type of a given speech segment. Specifically, its task is to extract the acoustic features that can express emotions from the collected speech signals, and then map these features to a certain type of emotion. [...

AI Technical Summary

Problems solved by technology

So far, neural network-based speech emotion recognition methods only learn emotional deep features from a single feature (such as spectral or hand-crafted features)

However, speech contains complex information, and various features can be extracted. Existing methods do not make full use of the complementarity between different features, which makes the modeling ability of speech emotion poor, resulting in relatively poor performance of emotion recognition. high

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