@inproceedings{ma-etal-2022-duo,
title = "多特征融合的越英端到端语音翻译方法(A {V}ietnamese-{E}nglish end-to-end speech translation method based on multi-feature fusion)",
author = "Ma, Houli and
Dong, Ling and
Wang, Wenjun and
Wang, Jian and
Gao, Shengxiang and
Yu, Zhengtao",
editor = "Sun, Maosong and
Liu, Yang and
Che, Wanxiang and
Feng, Yang and
Qiu, Xipeng and
Rao, Gaoqi and
Chen, Yubo",
booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Nanchang, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2022.ccl-1.27",
pages = "293--304",
abstract = "{``}语音翻译的编码器需要同时编码语音中的声学和语义信息,单一的Fbank或Wav2vec2语音特征表征能力存在不足。本文通过分析人工的Fbank特征与自监督的Wav2vec2特征间的差异性,提出基于交叉注意力机制的声学特征融合方法,并探究了不同的自监督特征和融合方式,加强模型对语音中声学和语义信息的学习。结合越南语语音特点,以Fbank特征为主、Pitch特征为辅混合编码Fbank表征,构建多特征融合的越-英语音翻译模型。实验表明,使用多特征的语音翻译模型相比单特征翻译效果更优,与简单的特征拼接方法相比更有效,所提的多特征融合方法在越-英语音翻译任务上提升了1.97个BLEU值。{''}",
language = "Chinese",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ma-etal-2022-duo">
<titleInfo>
<title>多特征融合的越英端到端语音翻译方法(A Vietnamese-English end-to-end speech translation method based on multi-feature fusion)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Houli</namePart>
<namePart type="family">Ma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ling</namePart>
<namePart type="family">Dong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wenjun</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jian</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shengxiang</namePart>
<namePart type="family">Gao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhengtao</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">Chinese</languageTerm>
<languageTerm type="code" authority="iso639-2b">chi</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 21st Chinese National Conference on Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maosong</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wanxiang</namePart>
<namePart type="family">Che</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Feng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xipeng</namePart>
<namePart type="family">Qiu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gaoqi</namePart>
<namePart type="family">Rao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yubo</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Nanchang, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“语音翻译的编码器需要同时编码语音中的声学和语义信息,单一的Fbank或Wav2vec2语音特征表征能力存在不足。本文通过分析人工的Fbank特征与自监督的Wav2vec2特征间的差异性,提出基于交叉注意力机制的声学特征融合方法,并探究了不同的自监督特征和融合方式,加强模型对语音中声学和语义信息的学习。结合越南语语音特点,以Fbank特征为主、Pitch特征为辅混合编码Fbank表征,构建多特征融合的越-英语音翻译模型。实验表明,使用多特征的语音翻译模型相比单特征翻译效果更优,与简单的特征拼接方法相比更有效,所提的多特征融合方法在越-英语音翻译任务上提升了1.97个BLEU值。”</abstract>
<identifier type="citekey">ma-etal-2022-duo</identifier>
<location>
<url>https://aclanthology.org/2022.ccl-1.27</url>
</location>
<part>
<date>2022-10</date>
<extent unit="page">
<start>293</start>
<end>304</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T 多特征融合的越英端到端语音翻译方法(A Vietnamese-English end-to-end speech translation method based on multi-feature fusion)
%A Ma, Houli
%A Dong, Ling
%A Wang, Wenjun
%A Wang, Jian
%A Gao, Shengxiang
%A Yu, Zhengtao
%Y Sun, Maosong
%Y Liu, Yang
%Y Che, Wanxiang
%Y Feng, Yang
%Y Qiu, Xipeng
%Y Rao, Gaoqi
%Y Chen, Yubo
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G Chinese
%F ma-etal-2022-duo
%X “语音翻译的编码器需要同时编码语音中的声学和语义信息,单一的Fbank或Wav2vec2语音特征表征能力存在不足。本文通过分析人工的Fbank特征与自监督的Wav2vec2特征间的差异性,提出基于交叉注意力机制的声学特征融合方法,并探究了不同的自监督特征和融合方式,加强模型对语音中声学和语义信息的学习。结合越南语语音特点,以Fbank特征为主、Pitch特征为辅混合编码Fbank表征,构建多特征融合的越-英语音翻译模型。实验表明,使用多特征的语音翻译模型相比单特征翻译效果更优,与简单的特征拼接方法相比更有效,所提的多特征融合方法在越-英语音翻译任务上提升了1.97个BLEU值。”
%U https://aclanthology.org/2022.ccl-1.27
%P 293-304
Markdown (Informal)
[多特征融合的越英端到端语音翻译方法(A Vietnamese-English end-to-end speech translation method based on multi-feature fusion)](https://aclanthology.org/2022.ccl-1.27) (Ma et al., CCL 2022)
ACL