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 HMM 기반 음성 인식기에서 음향 우도 계산을 가속화하기 위한 상태 우도 재활용과 배치 상태 우도 계산의 효율적인 조합을 제안합니다. 재활용 및 배치 계산은 각각 서로 다른 기술적 접근 방식을 기반으로 합니다. 즉, 전자는 순전히 알고리즘 기술인 반면 후자는 컴퓨터 아키텍처를 완전히 활용합니다. 효율적으로 결합하여 인식 프로세스를 더욱 가속화하기 위해 다음을 소개합니다. 조건부 빠른 처리 and 음향 백오프. 조건부 빠른 처리는 두 가지 기준을 기반으로 합니다. 첫번째 잠재적인 활동 기준은 현재 프레임에서 상태 가능성의 재활용뿐만 아니라 여러 후속 프레임에 대한 상태 가능성의 사전 계산을 제어하는 데 사용됩니다. 두번째 신뢰성 기준 및 음향 백킹은 조합에서 모순되는 경우 재활용 또는 일괄 계산된 상태 가능성의 선택을 제어하고 단어 정확도가 저하되는 것을 방지하는 데 사용됩니다. 두 가지 환경 조건에서 4개의 서로 다른 CPU 시스템을 사용한 대규모 어휘 자발적 음성 인식 실험은 기본 인식기, 재활용 및 일괄 계산과 비교하여 결합된 가속 기술이 음향 우도 계산 시간과 총 인식 시간을 모두 더 감소시키는 것으로 나타났습니다. 또한 상태 가능성 유형을 분류하고 각각의 개수를 계산하여 각 기술의 가속 및 환경 의존성 메커니즘을 밝히기 위한 세부 분석을 수행했습니다. 분석 결과, 복합가속기법의 유효성이 확인되었다.
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Atsunori OGAWA, Satoshi TAKAHASHI, Atsushi NAKAMURA, "Efficient Combination of Likelihood Recycling and Batch Calculation for Fast Acoustic Likelihood Calculation" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 648-658, March 2011, doi: 10.1587/transinf.E94.D.648.
Abstract: This paper proposes an efficient combination of state likelihood recycling and batch state likelihood calculation for accelerating acoustic likelihood calculation in an HMM-based speech recognizer. Recycling and batch calculation are each based on different technical approaches, i.e. the former is a purely algorithmic technique while the latter fully exploits computer architecture. To accelerate the recognition process further by combining them efficiently, we introduce conditional fast processing and acoustic backing-off. Conditional fast processing is based on two criteria. The first potential activity criterion is used to control not only the recycling of state likelihoods at the current frame but also the precalculation of state likelihoods for several succeeding frames. The second reliability criterion and acoustic backing-off are used to control the choice of recycled or batch calculated state likelihoods when they are contradictory in the combination and to prevent word accuracies from degrading. Large vocabulary spontaneous speech recognition experiments using four different CPU machines under two environmental conditions showed that, compared with the baseline recognizer, recycling and batch calculation, our combined acceleration technique further reduced both of the acoustic likelihood calculation time and the total recognition time. We also performed detailed analyses to reveal each technique's acceleration and environmental dependency mechanisms by classifying types of state likelihoods and counting each of them. The analysis results comfirmed the effectiveness of the combined acceleration technique.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.648/_p
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@ARTICLE{e94-d_3_648,
author={Atsunori OGAWA, Satoshi TAKAHASHI, Atsushi NAKAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Combination of Likelihood Recycling and Batch Calculation for Fast Acoustic Likelihood Calculation},
year={2011},
volume={E94-D},
number={3},
pages={648-658},
abstract={This paper proposes an efficient combination of state likelihood recycling and batch state likelihood calculation for accelerating acoustic likelihood calculation in an HMM-based speech recognizer. Recycling and batch calculation are each based on different technical approaches, i.e. the former is a purely algorithmic technique while the latter fully exploits computer architecture. To accelerate the recognition process further by combining them efficiently, we introduce conditional fast processing and acoustic backing-off. Conditional fast processing is based on two criteria. The first potential activity criterion is used to control not only the recycling of state likelihoods at the current frame but also the precalculation of state likelihoods for several succeeding frames. The second reliability criterion and acoustic backing-off are used to control the choice of recycled or batch calculated state likelihoods when they are contradictory in the combination and to prevent word accuracies from degrading. Large vocabulary spontaneous speech recognition experiments using four different CPU machines under two environmental conditions showed that, compared with the baseline recognizer, recycling and batch calculation, our combined acceleration technique further reduced both of the acoustic likelihood calculation time and the total recognition time. We also performed detailed analyses to reveal each technique's acceleration and environmental dependency mechanisms by classifying types of state likelihoods and counting each of them. The analysis results comfirmed the effectiveness of the combined acceleration technique.},
keywords={},
doi={10.1587/transinf.E94.D.648},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - Efficient Combination of Likelihood Recycling and Batch Calculation for Fast Acoustic Likelihood Calculation
T2 - IEICE TRANSACTIONS on Information
SP - 648
EP - 658
AU - Atsunori OGAWA
AU - Satoshi TAKAHASHI
AU - Atsushi NAKAMURA
PY - 2011
DO - 10.1587/transinf.E94.D.648
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - This paper proposes an efficient combination of state likelihood recycling and batch state likelihood calculation for accelerating acoustic likelihood calculation in an HMM-based speech recognizer. Recycling and batch calculation are each based on different technical approaches, i.e. the former is a purely algorithmic technique while the latter fully exploits computer architecture. To accelerate the recognition process further by combining them efficiently, we introduce conditional fast processing and acoustic backing-off. Conditional fast processing is based on two criteria. The first potential activity criterion is used to control not only the recycling of state likelihoods at the current frame but also the precalculation of state likelihoods for several succeeding frames. The second reliability criterion and acoustic backing-off are used to control the choice of recycled or batch calculated state likelihoods when they are contradictory in the combination and to prevent word accuracies from degrading. Large vocabulary spontaneous speech recognition experiments using four different CPU machines under two environmental conditions showed that, compared with the baseline recognizer, recycling and batch calculation, our combined acceleration technique further reduced both of the acoustic likelihood calculation time and the total recognition time. We also performed detailed analyses to reveal each technique's acceleration and environmental dependency mechanisms by classifying types of state likelihoods and counting each of them. The analysis results comfirmed the effectiveness of the combined acceleration technique.
ER -