A Semi-Supervised Method for Decomposing Variable Factors of Speech Features

Abstract

The invention discloses a method for decomposing variable factors of semi-supervised speech features, which divides speech features into four categories: emotion-related features, gender-related features, age-related features, and other factor-related features including noise and language. First, the speech is preprocessed to obtain the spectrogram, and the spectrogram blocks of different sizes are input into the unsupervised feature learning network SAE, and the convolution kernels of different sizes are obtained through pre-training, and then the entire spectrogram is processed with convolution kernels of different sizes. Convolution to obtain several feature maps, and then perform maximum pooling on the feature maps, and finally stack the features to form a local invariant feature y. y is used as the input of the semi-supervised convolutional neural network, and y is decomposed into four types of features by minimizing four different loss function terms. The present invention solves the problem of low recognition accuracy caused by mixed voice features of emotion, gender and age, can be used for different recognition requirements based on voice signals, and can also be used to decompose more factors.

Description

technical field [0001] The invention belongs to the field of speech recognition, and in particular relates to a method for decomposing speech features. Background technique [0002] As computers penetrate into every corner of life, various types of computing platforms need easier input media, and voice is one of the best choices for users. Generally speaking, speech includes various information such as the speaker, the content of the speech, the emotion of the speaker, gender, age, etc. In recent years, with the continuous improvement of some applications, the development of speech signal-based recognition technology for human emotions, gender, age, speech content, etc. has been promoted. For example, traditional call centers usually randomly connect waiters to provide customers with telephone consultation, but cannot provide personalized services based on the user's emotion, gender and age, which prompts whether it is possible to judge the customer's emotion through the vo...

AI Technical Summary

Problems solved by technology

However, in the existing tasks related to emotion, gender and age recognition based on speech signals, the features extracted by traditional feature extraction methods are often mixed with factors such as emotion, gender, age, speech content, language, etc., which are difficult to compare with each other. distinction, resulting in poor recognition

[0003] In the paper titled Feature Learning in Deep Neural Networks—Studieson Speech Recognition Tasks by Dong Yu et al., a deep neural network is used to learn a deep feature, but this feature may be mixed with many factors, such as emotion, gender, age and other factors. If this feature is used for speech emotion recognition, the recognition rate may be affected by other factors in the feature

At present, there is no feature extraction method that can extract different types of features in speech signals.

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