System and method for tandem feature extraction for general speech tasks in speech signals

Abstract

The invention discloses a series feature extraction system and method for general speech tasks in speech signals. After the emotional corpus is established, the speech preprocessing is performed; the audio file after the speech preprocessing is used as an input and connected with the speech feature extraction model; the speech feature extraction model The basic information and salient features of the speaker can be obtained through the voiceprint recognition layer and fed into the speech recognition layer together to obtain speaker information and text information, which are sent to the emotion recognition layer together with low-level descriptors on the basis of keyword extraction. Use personalized voice features and emotional features to perform emotion recognition to obtain related emotional features; the voice feature extraction model can also obtain the basic information and salient features of the speaker through the voiceprint recognition layer, and feed them into the voice together with the features of voice recognition The recognition layer sends speaker information and low-level descriptors to the emotion recognition layer to output emotion features. The invention has the advantages of large data scale, accurate emotion expression, high integration degree and the like.

Description

technical field [0001] The invention relates to the field of signal processing and extraction, in particular to a feature extraction model of speech tasks. Background technique [0002] Speech is the most effective, natural and important form of communication for human beings. Realizing communication between humans and machines through voice requires machines to be intelligent enough to recognize human voices. With the development of machine learning, neural network and deep learning theory, the completion of speech recognition related tasks is gradually improving, which is of great help to the computer to understand the content of speech. Currently, speech recognition tasks mainly involve the following three recognition tasks: [0003] 1. Voiceprint recognition [0004] Voiceprint recognition, also known as speaker recognition, is a form of biometric recognition. It analyzes and processes the continuous speech signal of the speaker to extract discrete speech features, and...

AI Technical Summary

Problems solved by technology

[0009] However, there are many defects or deficiencies in the actual application or design of the above three recognition tasks

For example: Voiceprint recognition, speech recognition, and emotion recognition task models are not universal, input forms are not uniform, there is no universal solution, the integration accuracy is not high, the recognition accuracy rate of a single emotion recognition task is not high, etc.

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