Layer-by-layer channel selection method for voice recognition of self-organizing microphone

一种通道选择、语音识别的技术，应用在语音识别、语音分析、仪器等方向，能够解决性能没有帮助、网络计算量增大、没有探究通道选择等问题，达到识别性能提升、降低计算复杂度的效果

Pending Publication Date: 2021-11-09
NORTHWESTERN POLYTECHNICAL UNIV +1
View PDF0 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the above methods only consider the channel weight assignment of a small number of self-organizing nodes (no more than 10 microphone nodes), and do not explore the issue of channel selection
When the sound field environment becomes larger and more complex, and there are more self-organizing nodes, on the one hand, because some channels are greatly affected by noise, some channels that do not help the performance need to be discarded; on the other hand, due to the increase in the number of channels, the network The amount of calculation increases, so it is necessary to explore channel selection methods that reduce computational complexity

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

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment

[0129] This embodiment uses three data sets: the Librispeech corpus, the Libri-adhoc-simu data set based on the Librispeech simulation obtained under the self-organizing microphone array environment, and the Libri-adhoc40 in which 40 distributed microphones play back Librispeech in a real environment. Each node of the self-organizing microphone array of Libri-adhoc-simu and Libri-adhoc40 is a single microphone, and one channel represents one node. Librispeech contains more than 1000 hours of English speeches by 2484 speakers. In the embodiment, 960 hours of data are selected to train the single-channel ASR system, and 10 hours of data are selected for verification.

[0130] For the simulation data, Libri-adhoc-simu uses the 100-hour "train-100" subset of the Librispeech data as training data. Use the “dev-clean” subset as validation data, containing a total of 10 hours of data. Treat the "test-clean" subset as two separate test sets, each containing 5 hours of test data. Th...

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 layer-by-layer channel selection method for voice recognition of a self-organizing microphone, and the method is based on a conformer voice recognition framework, and the method comprises: (1), an encoder-decoder framework is adopted, an encoder is based on a Conformer framework, a decoder is based on a Transformer framework, and a multi-head attention mechanism is introduced into an encoder-decoder module; (2) for a single-channel voice recognition system, clean voice is adopted for independent training; and (3) for a multi-channel voice recognition system, the voice of each channel is encoded and then the same decoder is shared, a multi-layer flow attention mechanism is trained, and the channels are screened layer by layer. Under a large-scale self-organizing microphone array, compared with other flow attention-based methods, the method provided by the invention is higher in speech recognition accuracy and lower in calculation complexity.

Description

technical field [0001] The invention belongs to the technical field of voice recognition, and in particular relates to a layer-by-layer channel selection method for voice recognition. Background technique [0002] Long-distance speech recognition is an extremely challenging problem. Multi-channel speech recognition based on microphone array is an important way to improve performance. However, when the distance between the speaker and the microphone array increases, the quality of speech will drop sharply, resulting in a physical upper bound for the performance of Automatic Speech Recognition (ASR) no matter how many channels are added to the array. Self-organizing microphone array is a method to solve the above problems, which includes a series of microphone nodes randomly scattered in the sound field, and the microphone node can be a microphone or a microphone array. Using channel weight assignment and channel selection, the microphones around the speaker can be automatic...

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): G10L15/06G10L15/26G10L19/008G10L21/0216
CPCG10L15/063G10L15/26G10L19/008G10L21/0216G10L2021/02166
Inventor 张晓雷陈俊淇
Owner NORTHWESTERN POLYTECHNICAL UNIV
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
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap