Voice signal feature fusion method

Abstract

The invention discloses a novel voice signal feature fusion method. The method comprises the following steps: S1, constructing a model framework; S2, designing a neural network; and S3, carrying out audio feature representation and extraction. According to the method, a CNN variant and an LSTM variant are parallelly fused to form the novel hybrid neural network, meanwhile, the signal features having the maximal influences on the final emotion are extracted by combining with an Attention mechanism, finally, the audio emotion vector capable of more accurately and more rapidly classifying emotionis obtained, the generalization ability is strong, the structure is clear, the integration with or separation from other modules is easy, and in addition, the problem that the traditional voice signal features can not realize effective fusion is solved.

Description

technical field [0001] The invention relates to the technical field of speech signal processing artificial intelligence, in particular to a method for fusion of speech signal features. Background technique [0002] Sentiment analysis is a well-studied research field in natural language processing, which is the computational study of people's opinions, emotions, evaluations, and attitudes towards entities such as products, services, etc. [0003] Traditional sentiment analysis methods are mainly based on text. With the rapid development of communication technology, the abundance of smart phones and the rapid rise of social media, network users upload a large amount of data in the form of audio or video instead of text. Compared to video or audiovisual communication, speech as a modality seems to be the most suitable for sentiment analysis in humans. In fact, audio sentiment analysis is a difficult task due to the complexity of audio signals. As we all know, speech is the mo...

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Problems solved by technology

However, the image and video data are too complex, and the extrac

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