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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A detecção de termos falados consulta por exemplo (QbE-STD) é uma tarefa de usar consultas de fala para combinar enunciados, e o método de incorporação acústica de palavras (AWE) para gerar representações de comprimento fixo para segmentos de fala tem mostrado alto desempenho e eficiência em recente trabalhar. Propomos um método de treinamento AWE usando uma rede adversária de rótulos para reduzir as informações de interferência aprendidas durante o treinamento AWE. Experimentos demonstram que nosso método alcança melhorias significativas em conjuntos de testes multilíngues e sem recursos.
Zhaoqi LI
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Ta LI
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Qingwei ZHAO
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Pengyuan ZHANG
Chinese Academy of Sciences,University of Chinese Academy of Sciences
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Zhaoqi LI, Ta LI, Qingwei ZHAO, Pengyuan ZHANG, "Label-Adversarial Jointly Trained Acoustic Word Embedding" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 8, pp. 1501-1505, August 2022, doi: 10.1587/transinf.2022EDL8012.
Abstract: Query-by-example spoken term detection (QbE-STD) is a task of using speech queries to match utterances, and the acoustic word embedding (AWE) method of generating fixed-length representations for speech segments has shown high performance and efficiency in recent work. We propose an AWE training method using a label-adversarial network to reduce the interference information learned during AWE training. Experiments demonstrate that our method achieves significant improvements on multilingual and zero-resource test sets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8012/_p
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@ARTICLE{e105-d_8_1501,
author={Zhaoqi LI, Ta LI, Qingwei ZHAO, Pengyuan ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Label-Adversarial Jointly Trained Acoustic Word Embedding},
year={2022},
volume={E105-D},
number={8},
pages={1501-1505},
abstract={Query-by-example spoken term detection (QbE-STD) is a task of using speech queries to match utterances, and the acoustic word embedding (AWE) method of generating fixed-length representations for speech segments has shown high performance and efficiency in recent work. We propose an AWE training method using a label-adversarial network to reduce the interference information learned during AWE training. Experiments demonstrate that our method achieves significant improvements on multilingual and zero-resource test sets.},
keywords={},
doi={10.1587/transinf.2022EDL8012},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - Label-Adversarial Jointly Trained Acoustic Word Embedding
T2 - IEICE TRANSACTIONS on Information
SP - 1501
EP - 1505
AU - Zhaoqi LI
AU - Ta LI
AU - Qingwei ZHAO
AU - Pengyuan ZHANG
PY - 2022
DO - 10.1587/transinf.2022EDL8012
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2022
AB - Query-by-example spoken term detection (QbE-STD) is a task of using speech queries to match utterances, and the acoustic word embedding (AWE) method of generating fixed-length representations for speech segments has shown high performance and efficiency in recent work. We propose an AWE training method using a label-adversarial network to reduce the interference information learned during AWE training. Experiments demonstrate that our method achieves significant improvements on multilingual and zero-resource test sets.
ER -