The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 음성 인식을 위해 설계된 신호 향상의 이득 완화를 제안합니다. 이득 완화는 대상 신호 구성 요소의 바람직하지 않은 작은 왜곡으로 인해 발생하는 음성 인식의 잠재적인 저하를 제거하기 위해 대상 신호의 더 부드러운 향상을 선택적으로 적용합니다. 더 부드러운 향상은 인식할 수 없는 작은 로컬 소음원이 있는 상용 제품에서 발생하는 음성 인식과 결합된 신호 향상에서 간과된 성능 저하에 대한 솔루션입니다. 상업용 PC(개인용 컴퓨터)에 의해 녹음된 신호의 방향성 간섭 억제를 평가하면 깨끗한 음성 성능을 희생하지 않고도 입력에 대한 신호 향상이 달성된다는 것을 알 수 있습니다.
Ryoji MIYAHARA
NEC Platforms
Akihiko SUGIYAMA
NEC Corporation,Tokyo Metropolitan University
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부
Ryoji MIYAHARA, Akihiko SUGIYAMA, "Gain Relaxation: A Solution to Overlooked Performance Degradation in Speech Recognition with Signal Enhancement" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 11, pp. 1832-1840, November 2018, doi: 10.1587/transfun.E101.A.1832.
Abstract: This paper proposes gain relaxation in signal enhancement designed for speech recognition. Gain relaxation selectively applies softer enhancement of a target signal to eliminate potential degradation in speech recognition caused by small undesirable distortion in the target signal components. The softer enhancement is a solution to overlooked performance degradation in signal enhancement combined with speech recognition which is encountered in commercial products with an unaware small local noise source. Evaluation of directional interference suppression with signals recorded by a commercial PC (personal computer) demonstrates that signal enhancement over the input is achieved without sacrificing the performance for clean speech.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1832/_p
부
@ARTICLE{e101-a_11_1832,
author={Ryoji MIYAHARA, Akihiko SUGIYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Gain Relaxation: A Solution to Overlooked Performance Degradation in Speech Recognition with Signal Enhancement},
year={2018},
volume={E101-A},
number={11},
pages={1832-1840},
abstract={This paper proposes gain relaxation in signal enhancement designed for speech recognition. Gain relaxation selectively applies softer enhancement of a target signal to eliminate potential degradation in speech recognition caused by small undesirable distortion in the target signal components. The softer enhancement is a solution to overlooked performance degradation in signal enhancement combined with speech recognition which is encountered in commercial products with an unaware small local noise source. Evaluation of directional interference suppression with signals recorded by a commercial PC (personal computer) demonstrates that signal enhancement over the input is achieved without sacrificing the performance for clean speech.},
keywords={},
doi={10.1587/transfun.E101.A.1832},
ISSN={1745-1337},
month={November},}
부
TY - JOUR
TI - Gain Relaxation: A Solution to Overlooked Performance Degradation in Speech Recognition with Signal Enhancement
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1832
EP - 1840
AU - Ryoji MIYAHARA
AU - Akihiko SUGIYAMA
PY - 2018
DO - 10.1587/transfun.E101.A.1832
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2018
AB - This paper proposes gain relaxation in signal enhancement designed for speech recognition. Gain relaxation selectively applies softer enhancement of a target signal to eliminate potential degradation in speech recognition caused by small undesirable distortion in the target signal components. The softer enhancement is a solution to overlooked performance degradation in signal enhancement combined with speech recognition which is encountered in commercial products with an unaware small local noise source. Evaluation of directional interference suppression with signals recorded by a commercial PC (personal computer) demonstrates that signal enhancement over the input is achieved without sacrificing the performance for clean speech.
ER -