Interaction-oriented speech corpus processing method and device

Abstract

The embodiments of the invention provide an interaction-oriented speech corpus processing method and device. The method includes the following steps: a speech fragment is converted into a time-frequency graph; the features of the time-frequency graph are learned by using two convolution layers to get a feature graph matrix; the feature graph matrix is compressed by using a maximum pooling layer, and the compressed matrix is converted into a vector; and the generation of the vector is learned by using two LSTM layers, and the learned feature vector is taken as the input corpus of SVM. Therefore, the number of effective corpus can be increased, the training of a speech emotion recognition model can be facilitated, and the recognition ability of the speech emotion recognition model can be improved.

Description

technical field [0001] The present invention relates to the technical field of software, in particular to an interaction-oriented speech corpus processing method and device. Background technique [0002] Speech emotion recognition has the same characteristics as text and image recognition. There are three types of learning methods: supervised learning, semi-supervised learning and unsupervised learning. Most of the speech emotion recognition methods at this stage: first use traditional methods to extract features including speech rate, prosody intensity, Mel frequency cepstral coefficient, etc.; then use classifiers to classify, including support vector machines, Gaussian models and hidden Markov models and other classification methods. [0003] Similar emotion recognition methods have achieved many results, but there are still some shortcomings. In the current speech emotion recognition, it is not clear which features have a greater impact on the classification of emotion...

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Problems solved by technology

[0005] However, in the process of implementing the embodiments of the present invention, the inventors found that for the existing speech emotion recognition model, due to the limited number of corpus used for training and the difficulty of collecting appropriate corpus, the recognition ability of the speech emotion recognition model limited

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