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
음성 인식에서 신뢰도 주석은 분류를 위해 단일 신뢰도 기능 또는 다양한 기능의 조합을 채택합니다. 이러한 신뢰도 특징은 항상 디코딩 정보에서 추출됩니다. 그러나 인간의 음성 이해에 관한 지식의 약 30%는 주로 상위 수준의 정보에서 파생된다는 것이 입증되었습니다. 따라서, 디코딩 정보와 통계적으로 독립적인 높은 수준의 신뢰도 특징을 추출하는 방법은 음성 인식에서 연구할 가치가 있습니다. 본 논문에서는 잠재 주제 유사성에 기반한 새로운 신뢰 특징 추출 알고리즘을 제안합니다. 하나의 인식 결과에서 각 단어 주제 분포와 문맥 주제 분포는 LDA(Latent Dirichlet Allocation) 주제 모델을 사용하여 먼저 구한 후, 두 주제 분포 간의 유사성을 판단하여 제안된 단어 신뢰도 특성을 추출합니다. 실험을 통해 제안된 특징은 좋은 정보 보완 효과로 신뢰 특징의 정보 소스 수를 증가시키고, 디코딩 정보의 신뢰 특징과 결합된 신뢰 주석의 성능을 효과적으로 향상시킬 수 있음을 보여줍니다.
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부
Wei CHEN, Gang LIU, Jun GUO, Shinichiro OMACHI, Masako OMACHI, Yujing GUO, "Novel Confidence Feature Extraction Algorithm Based on Latent Topic Similarity" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 8, pp. 2243-2251, August 2010, doi: 10.1587/transinf.E93.D.2243.
Abstract: In speech recognition, confidence annotation adopts a single confidence feature or a combination of different features for classification. These confidence features are always extracted from decoding information. However, it is proved that about 30% of knowledge of human speech understanding is mainly derived from high-level information. Thus, how to extract a high-level confidence feature statistically independent of decoding information is worth researching in speech recognition. In this paper, a novel confidence feature extraction algorithm based on latent topic similarity is proposed. Each word topic distribution and context topic distribution in one recognition result is firstly obtained using the latent Dirichlet allocation (LDA) topic model, and then, the proposed word confidence feature is extracted by determining the similarities between these two topic distributions. The experiments show that the proposed feature increases the number of information sources of confidence features with a good information complementary effect and can effectively improve the performance of confidence annotation combined with confidence features from decoding information.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2243/_p
부
@ARTICLE{e93-d_8_2243,
author={Wei CHEN, Gang LIU, Jun GUO, Shinichiro OMACHI, Masako OMACHI, Yujing GUO, },
journal={IEICE TRANSACTIONS on Information},
title={Novel Confidence Feature Extraction Algorithm Based on Latent Topic Similarity},
year={2010},
volume={E93-D},
number={8},
pages={2243-2251},
abstract={In speech recognition, confidence annotation adopts a single confidence feature or a combination of different features for classification. These confidence features are always extracted from decoding information. However, it is proved that about 30% of knowledge of human speech understanding is mainly derived from high-level information. Thus, how to extract a high-level confidence feature statistically independent of decoding information is worth researching in speech recognition. In this paper, a novel confidence feature extraction algorithm based on latent topic similarity is proposed. Each word topic distribution and context topic distribution in one recognition result is firstly obtained using the latent Dirichlet allocation (LDA) topic model, and then, the proposed word confidence feature is extracted by determining the similarities between these two topic distributions. The experiments show that the proposed feature increases the number of information sources of confidence features with a good information complementary effect and can effectively improve the performance of confidence annotation combined with confidence features from decoding information.},
keywords={},
doi={10.1587/transinf.E93.D.2243},
ISSN={1745-1361},
month={August},}
부
TY - JOUR
TI - Novel Confidence Feature Extraction Algorithm Based on Latent Topic Similarity
T2 - IEICE TRANSACTIONS on Information
SP - 2243
EP - 2251
AU - Wei CHEN
AU - Gang LIU
AU - Jun GUO
AU - Shinichiro OMACHI
AU - Masako OMACHI
AU - Yujing GUO
PY - 2010
DO - 10.1587/transinf.E93.D.2243
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2010
AB - In speech recognition, confidence annotation adopts a single confidence feature or a combination of different features for classification. These confidence features are always extracted from decoding information. However, it is proved that about 30% of knowledge of human speech understanding is mainly derived from high-level information. Thus, how to extract a high-level confidence feature statistically independent of decoding information is worth researching in speech recognition. In this paper, a novel confidence feature extraction algorithm based on latent topic similarity is proposed. Each word topic distribution and context topic distribution in one recognition result is firstly obtained using the latent Dirichlet allocation (LDA) topic model, and then, the proposed word confidence feature is extracted by determining the similarities between these two topic distributions. The experiments show that the proposed feature increases the number of information sources of confidence features with a good information complementary effect and can effectively improve the performance of confidence annotation combined with confidence features from decoding information.
ER -