@inproceedings{shanglong-etal-2024-ji,
title = "基于预训练模型与序列建模的音素分割方法(Sequence Modeling)",
author = "Shanglong, Yang and
Zhengtao, Yu and
Wenjun, Wang and
Ling, Dong and
Shengxiang, Gao",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.49/",
pages = "625--636",
language = "zho",
abstract = "{\textquotedblleft}音素分割作为语音处理领域内的一个重要任务,对于关键词识别、自动语音识别等应用具有至关重要的意义。传统方法往往独立预测每一帧音频是否为音素边界,忽视了音素边界与整个音频序列以及相邻帧之间的内在联系,从而影响了分割的准确性和连贯性。本文提出一种基于预训练模型与序列建模的音素分割方法,在HuBERT模型提取声学特征的基础上,结合BiLSTM捕捉长期依赖,再用CRF优化序列,提升了音素边界检测的性能。在TIMIT和Buckeye数据集上的实验表明,本文方法优于现有技术,证明了序列建模在音素分割任务中的有效性。{\textquotedblright}"
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="shanglong-etal-2024-ji">
<titleInfo>
<title>基于预训练模型与序列建模的音素分割方法(Sequence Modeling)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Shanglong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu</namePart>
<namePart type="family">Zhengtao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wang</namePart>
<namePart type="family">Wenjun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dong</namePart>
<namePart type="family">Ling</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gao</namePart>
<namePart type="family">Shengxiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">zho</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</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">Jiye</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianpei</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiyuan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yulan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“音素分割作为语音处理领域内的一个重要任务,对于关键词识别、自动语音识别等应用具有至关重要的意义。传统方法往往独立预测每一帧音频是否为音素边界,忽视了音素边界与整个音频序列以及相邻帧之间的内在联系,从而影响了分割的准确性和连贯性。本文提出一种基于预训练模型与序列建模的音素分割方法,在HuBERT模型提取声学特征的基础上,结合BiLSTM捕捉长期依赖,再用CRF优化序列,提升了音素边界检测的性能。在TIMIT和Buckeye数据集上的实验表明,本文方法优于现有技术,证明了序列建模在音素分割任务中的有效性。”</abstract>
<identifier type="citekey">shanglong-etal-2024-ji</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.49/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>625</start>
<end>636</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T 基于预训练模型与序列建模的音素分割方法(Sequence Modeling)
%A Shanglong, Yang
%A Zhengtao, Yu
%A Wenjun, Wang
%A Ling, Dong
%A Shengxiang, Gao
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F shanglong-etal-2024-ji
%X “音素分割作为语音处理领域内的一个重要任务,对于关键词识别、自动语音识别等应用具有至关重要的意义。传统方法往往独立预测每一帧音频是否为音素边界,忽视了音素边界与整个音频序列以及相邻帧之间的内在联系,从而影响了分割的准确性和连贯性。本文提出一种基于预训练模型与序列建模的音素分割方法,在HuBERT模型提取声学特征的基础上,结合BiLSTM捕捉长期依赖,再用CRF优化序列,提升了音素边界检测的性能。在TIMIT和Buckeye数据集上的实验表明,本文方法优于现有技术,证明了序列建模在音素分割任务中的有效性。”
%U https://aclanthology.org/2024.ccl-1.49/
%P 625-636
Markdown (Informal)
[基于预训练模型与序列建模的音素分割方法(Sequence Modeling)](https://aclanthology.org/2024.ccl-1.49/) (Shanglong et al., CCL 2024)
ACL
- Yang Shanglong, Yu Zhengtao, Wang Wenjun, Dong Ling, and Gao Shengxiang. 2024. 基于预训练模型与序列建模的音素分割方法(Sequence Modeling). In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 625–636, Taiyuan, China. Chinese Information Processing Society of China.