國立虎尾科技大學 |

Compressive nonlinearity for representing speech spectral magnitude to improve noise robustness of automatic speech recognition .

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Compressive nonlinearity for representing speech spectral magnitude to improve noise robustness of automatic speech recognition ./
作者:	Wong, Brian.
面頁冊數:	78 p.
附註:	Source: Masters Abstracts International, Volume: 50-02, page: 1213.
Contained By:	Masters Abstracts International50-02.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1499664
ISBN:	9781124881225

Compressive nonlinearity for representing speech spectral magnitude to improve noise robustness of automatic speech recognition .
Wong, Brian.

Compressive nonlinearity for representing speech spectral magnitude to improve noise robustness of automatic speech recognition . - 78 p.

Source: Masters Abstracts International, Volume: 50-02, page: 1213.

Thesis (M.S.)--State University of New York at Binghamton, 2011.

Current automatic speech recognition systems are able to perform fairly well for noise-free speech, but encounter problems when used in noisy environments. This is mainly due to the mismatch between the environment and type of noise found in the training data used to develop the system, and the conditions under which the system is actually used. This work explores the use of spectral range limiting functions to improve the noise robustness of automatic speech recognition systems. This is motivated by the compressive nonlinearities commonly found in auditory models and the fact that human speech recognition is superior to any automatic speech recognition system. For this reason, many automatic speech recognition techniques are motivated by auditory models and physiology of human and animal hearing systems. The goal is to combine the compressive nonlinear stage of an auditory model with traditional automatic speech recognition techniques to improve the accuracy of such systems.

ISBN: 9781124881225Subjects--Topical Terms:

845382
Engineering, Electronics and Electrical.

Compressive nonlinearity for representing speech spectral magnitude to improve noise robustness of automatic speech recognition .
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245 1 0 $a Compressive nonlinearity for representing speech spectral magnitude to improve noise robustness of automatic speech recognition .
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500 $a Source: Masters Abstracts International, Volume: 50-02, page: 1213.
500 $a Adviser: Stephen A. Zahorian.
502 $a Thesis (M.S.)--State University of New York at Binghamton, 2011.
520 $a Current automatic speech recognition systems are able to perform fairly well for noise-free speech, but encounter problems when used in noisy environments. This is mainly due to the mismatch between the environment and type of noise found in the training data used to develop the system, and the conditions under which the system is actually used. This work explores the use of spectral range limiting functions to improve the noise robustness of automatic speech recognition systems. This is motivated by the compressive nonlinearities commonly found in auditory models and the fact that human speech recognition is superior to any automatic speech recognition system. For this reason, many automatic speech recognition techniques are motivated by auditory models and physiology of human and animal hearing systems. The goal is to combine the compressive nonlinear stage of an auditory model with traditional automatic speech recognition techniques to improve the accuracy of such systems.
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