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あぁかがさざただなはばぱまやゃらわゎんいぃきぎしじちぢにひびぴみりうぅくぐすずつづっぬふぶぷむゆゅるえぇけげせぜてでねへべぺめれおぉこごそぞとどのほぼぽもよょろを

アァカサザタダナハバパマヤャラワヮンイィキギシジチヂニヒビピミリウゥクグスズツヅッヌフブプムユュルエェケゲセゼテデヘベペメレオォコゴソゾトドノホボポモヨョロヲ ―

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中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
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á à Á À é è É È ç Ç ê

Ä Ö Ü ä ö ü ß

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½ ⅓ ⅔ ¼ ¾ ⅛ ⅜ ⅝ ⅞ ¹ ² ³ ⁴ ⁿ ₁ ₂ ₃ ₄

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RISS 인기검색어

확산필터뱅크의 음성인식에 대한 성능 평가 = The Evaluation of a performance of the Diffusion Filter Bank on the Speech Recognition

한글로보기

https://www.riss.kr/link?id=A2054901

저자

許萬鐸 (공과대학 전자공학과) ; 金康彦 (공과대학 전자공학과)
발행기관
釜慶大學校
학술지명
釜慶大學校論文集(PUKYONG NATIONAL UNIVERSITY)
권호사항

Vol.1 No.2 [1996]
발행연도
1996
작성언어
Korean
KDC
040.000
자료형태
학술저널
수록면

85-93(9쪽)
제공처
RISS
소장기관
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다국어 초록 (Multilingual Abstract)

In this paper, we improved analyzing method of a spectrum envelope¹ using the diffusion filter bank.???? We increased the number of channel of filter bank. By dividing analysis band into subbands, we decreased the number of diffusion process. And, by increasing the number of difference, we got higher selectivity. As a result of them, we reduced the total processing time, and got higher enhancement of discrimination. Through computer simulation, we showed that average recognition rate for single vowels of real voice is 88.3[%]. Hence We confirmed it to be useful for speech recognition which use spectrum analysis to analyze frequency components.⁴

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In this paper, we improved analyzing method of a spectrum envelope¹ using the diffusion filter bank.???? We increased the number of channel of filter bank. By dividing analysis band into subbands, we decreased the number of diffusion process. And, by...

In this paper, we improved analyzing method of a spectrum envelope¹ using the diffusion filter bank.???? We increased the number of channel of filter bank. By dividing analysis band into subbands, we decreased the number of diffusion process. And, by increasing the number of difference, we got higher selectivity. As a result of them, we reduced the total processing time, and got higher enhancement of discrimination. Through computer simulation, we showed that average recognition rate for single vowels of real voice is 88.3[%]. Hence We confirmed it to be useful for speech recognition which use spectrum analysis to analyze frequency components.⁴