A hybrid design of beamformers for voice control devices
(2012) In Pacific Journal of Optimization 8(3). p.533-544- Abstract
- In this paper, a new approach to designing beamformers for voice control device is proposed. It is well-known that under a strong near-field noise with low signal-to-noise ratios (SNR), the performance of speech recognition is deteriorated significantly. However, designing the beamformer for enhancing speech recognition is a slow process and might not be adapted. easily to the changing noise environment. In order to lower the complexity of the design, we intend to exploit the combination of existing optimal beamformer designs, which can be implemented in parallel. These include the least-squares technique and the signal-to-noise ratio maximization technique. We show here that for a given pre-trained speech recognizer and for a finite set... (More)
- In this paper, a new approach to designing beamformers for voice control device is proposed. It is well-known that under a strong near-field noise with low signal-to-noise ratios (SNR), the performance of speech recognition is deteriorated significantly. However, designing the beamformer for enhancing speech recognition is a slow process and might not be adapted. easily to the changing noise environment. In order to lower the complexity of the design, we intend to exploit the combination of existing optimal beamformer designs, which can be implemented in parallel. These include the least-squares technique and the signal-to-noise ratio maximization technique. We show here that for a given pre-trained speech recognizer and for a finite set of speech commands, neither method has a satisfactory performance in speech recognition accuracy under very low SNRs. However, since the two techniques have different characteristics in speech distortion and noise suppression, we show that it is possible to enhance the speech recognition accuracy by combining these two optimal designs. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3fefb5f3-c09a-4c45-92fa-ea9167c716b3
- author
- Yui, K.F.C ; Chan, K.Y ; Grbic, Nedelko LU and Nordholm, Sven
- publishing date
- 2012
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Pacific Journal of Optimization
- volume
- 8
- issue
- 3
- pages
- 533 - 544
- publisher
- Yokohama Publishers
- external identifiers
-
- scopus:84874957516
- ISSN
- 1348-9151
- language
- English
- LU publication?
- no
- id
- 3fefb5f3-c09a-4c45-92fa-ea9167c716b3
- date added to LUP
- 2016-06-23 14:16:23
- date last changed
- 2024-01-04 08:54:42
@article{3fefb5f3-c09a-4c45-92fa-ea9167c716b3,
abstract = {{In this paper, a new approach to designing beamformers for voice control device is proposed. It is well-known that under a strong near-field noise with low signal-to-noise ratios (SNR), the performance of speech recognition is deteriorated significantly. However, designing the beamformer for enhancing speech recognition is a slow process and might not be adapted. easily to the changing noise environment. In order to lower the complexity of the design, we intend to exploit the combination of existing optimal beamformer designs, which can be implemented in parallel. These include the least-squares technique and the signal-to-noise ratio maximization technique. We show here that for a given pre-trained speech recognizer and for a finite set of speech commands, neither method has a satisfactory performance in speech recognition accuracy under very low SNRs. However, since the two techniques have different characteristics in speech distortion and noise suppression, we show that it is possible to enhance the speech recognition accuracy by combining these two optimal designs.}},
author = {{Yui, K.F.C and Chan, K.Y and Grbic, Nedelko and Nordholm, Sven}},
issn = {{1348-9151}},
language = {{eng}},
number = {{3}},
pages = {{533--544}},
publisher = {{Yokohama Publishers}},
series = {{Pacific Journal of Optimization}},
title = {{A hybrid design of beamformers for voice control devices}},
volume = {{8}},
year = {{2012}},
}