Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Speech recognition method based on domain-invariant feature

A speech recognition, domain-invariant technology, used in speech recognition, speech analysis, instruments, etc.

Active Publication Date: 2019-12-13
WUHAN UNIV OF TECH
View PDF11 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] There is currently no method for applying speech domain-invariant feature extraction models to end-to-end speech recognition models

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Speech recognition method based on domain-invariant feature
  • Speech recognition method based on domain-invariant feature
  • Speech recognition method based on domain-invariant feature

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0050] In order to specifically illustrate the purpose, technical solution, advantages and realizability of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific examples described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not constitute conflicts with each other.

[0051] Such as figure 1 Shown, a kind of speech recognition method based on domain invariant feature, this method comprises the following steps:

[0052] Step 1, constructing the training data set, including two main sub-steps of collecting speech data under different noise environments and annotating the content text corresponding to the speech, as follows:

[0053] (1.1) Collect...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a speech recognition method based on a domain-invariant feature, and the method applies a speech domain-invariant feature extraction model to an end-to-end speech recognition model. The feature extraction model used in the speech recognition method aims at the robustness problem, and by adding more types of speech data to train the speech feature extraction model, better parameters can be obtained and a better domain-invariant feature extraction model can be obtained. The speech recognition method based on the domain-invariant feature uses unlabeled pure speech data totrain the feature extraction model, uses a small number of speeches with text annotation to train an end-to-end acoustic model, and provides important technical support for improving the robustness ofthe end-to-end acoustic model. Compared with the prior art, the speech recognition method based on the domain-invariant feature has higher recognition accuracy in different noise environments, smaller task quantity of speech annotation tasks, and faster training and testing speed of the models.

Description

technical field [0001] The invention belongs to the field of speech recognition, and relates to a robust speech recognition method in real noise environments, specifically a speech recognition method based on domain invariant features, which can be quickly and conveniently extended to new noise environments. Background technique [0002] In recent years, the application of end-to-end speech recognition models based on deep learning and sequence-to-sequence computing frameworks has become increasingly widespread. However, in the process of actually using speech recognition models, it is inevitable to encounter a variety of noise environments. The recognition accuracy is greatly reduced. Noise robustness refers to the ability of a speech recognition model to maintain the original recognition accuracy in a noisy environment. [0003] At present, the common methods to improve the noise robustness of the speech recognition model are: (1) adding a feature enhancement model for sp...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G10L15/20G10L15/06G10L15/16
CPCG10L15/063G10L15/16G10L15/20
Inventor 熊盛武李梦林泽华徐珊李小其董元杰路雄博刁月月
Owner WUHAN UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products