Memory and attention model-based auditory selection method and device

An attention model and memory technology, applied in neural learning methods, biological neural network models, speech analysis, etc., can solve problems such as uncertain number of speaker aliasing and fixed memory unit dimensions

Active Publication Date: 2018-06-01
INST OF AUTOMATION CHINESE ACAD OF SCI
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  • Abstract
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Problems solved by technology

[0005] In order to solve the above problems in the prior art, that is, in order to solve the problems of the arrangement of supervised labels, the uncertain number of speaker aliasing and the

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  • Memory and attention model-based auditory selection method and device
  • Memory and attention model-based auditory selection method and device
  • Memory and attention model-based auditory selection method and device

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Embodiment Construction

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention

[0054] Preferred embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention, and are not intended to limit the protection scope of the present invention.

[005...

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Abstract

The invention belongs to the technical field of speech separation and particularly relates to a memory and attention model-based auditory selection method and device, so as to solve the problems of supervision label arrangement, speaker aliasing number uncertainty and memory unit dimension fixation in the prior art. The memory and attention model-based auditory selection method comprises steps: original speech signals are coded as a time frequency matrix, the time frequency matrix is subjected to coding and transformation, the time frequency matrix is converted to speech vectors, a long term memory unit is used to store speakers and corresponding speech vectors, the speech vector of a target speaker is acquired, and target speech is separated from the original speech signals through an attention selection model. The method provided in the invention does not need to fix or designate the number of speakers but can separate the target speech from the original speech signals.

Description

technical field [0001] The invention belongs to the technical field of speech separation, and in particular relates to an auditory selection method and device based on memory and attention models. Background technique [0002] In recent years, with the rapid development of electronic equipment and artificial intelligence, human-computer voice interaction, as an important part of the field of artificial intelligence, has become increasingly important, and human-computer voice interaction has been widely used in real life. Human-computer voice interaction is the machine recognition and analysis to extract the semantic feature information of the voice signal, compare it with the semantic features in the standard information base, and output the corresponding text or convert it into the output result we want. However, in practical applications, there are a lot of interference in the real environment, and the process of machine recognition, analysis and extraction of semantic fea...

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Application Information

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IPC IPC(8): G10L15/22G10L19/00G10L21/0208G10L21/0272G10L25/30
CPCG10L15/22G10L19/0017G10L21/0208G10L21/0272G10L25/30G10L2021/02087G06N3/08G06N3/044G06F17/16G06N3/049
Inventor 许家铭石晶徐波
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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