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基于稀疏编码的鲁棒说话人识别
  • ISSN号：1004-9037
  • 期刊名称：《数据采集与处理》
  • 分类：TP391.42[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]河南理工大学计算机科学与技术学院,河南焦作454000, [2]中国科学院自动化研究所模式识别国家重点实验室,北京100190
  • 相关基金：国家自然科学基金资助项目（91120303,90820303,90820011）;河南省基础与前沿技术研究计划资助项目（132300410332）
作者： 何勇军[1], 孙广路[1], 付茂国[1], 韩纪庆[2]
关键词： 语音识别, 随机段模型, 发音信息, 阶层式人工神经网路, 发音特征, speech recognition, stochastic segment model, articulatory information, hierarchical artificial neural network, articulatory feature
中文摘要：

提出了一种基于随机段模型的发音信息集成方法。根据随机段模型的模型特性，建立了阶层式人工神经网络来获取语音段信号属于各类音素的后验概率，并通过一遍解码的方式集成到随机段模型系统中。在“863-test”测试集上进行的汉语连续语音识别实验显示汉语字的相对错误率下降了5．93％。实验结果表明了将发音信息应用到随机段模型的可行性。

英文摘要：

This paper proposed a framework which attempted to incorporate articulatory information into the stochastic segment model based on Mandarin speech recognition system.According to the characteristics of the stochastic segment model,it used hierarchical artificial neural network to obtain the posterior probability of speech signal belonging to the phonemes.Then,it integrated the posterior probability into the stochastic segment model system in the first search process.Experiments conducted on “863-test”set show that about 5 .93% relative improvement could be achieved in the recognition accuracy.Thus,it de-monstrates the feasibility of the method.

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期刊信息
  • 《数据采集与处理》
  • 北大核心期刊（2011版）
  • 主管单位:中国科学技术协会
  • 主办单位:中国电子学会 仪器仪表学会 信号处理学会 中国一汽仪表学会 中国物理学会 微弱信号检测学会 南京航空航天大学
  • 主编：贲德
  • 地址：南京市御道街29号
  • 邮编：210016
  • 邮箱：sjcj@nuaa.edu.cn
  • 电话：025-84892742
  • 国际标准刊号：ISSN：1004-9037
  • 国内统一刊号：ISSN：32-1367/TN
  • 邮发代号:28-235
  • 获奖情况:
  • 中国科技论文统计源用刊,2007年被评为江苏省优秀期刊
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,荷兰文摘与引文数据库,美国剑桥科学文摘,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）
  • 被引量:8148