基于稀疏组LASSO约束的本征音子说话人自适应

屈丹; 张文林

doi:10.11959/j.issn.1000-436x.2015241

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基于稀疏组LASSO约束的本征音子说话人自适应

学术论文 | 更新时间：2024-06-05

- 基于稀疏组LASSO约束的本征音子说话人自适应
- Sparse group LASSO constraint eigenphone speaker adaptation method for speech recognition
- 通信学报 2015年36卷第9期页码：47-54
- 作者机构：
  
  信息工程大学信息系统工程学院，河南郑州 450000
- 作者简介：
  
  [ "屈丹（1974-），女，吉林长春人，博士，信息工程大学副教授、硕士生导师，主要研究方向为语音处理与识别、机器学习、自然语言处理。" ]
  [ "张文林（1982-），男，湖北蕲春人，博士，信息工程大学讲师，主要研究方向为语音处理与识别、机器学习、自然语言处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61175017);国家自然科学基金资助项目(61302107);国家自然科学基金资助项目(61403415)
- DOI：10.11959/j.issn.1000-436x.2015241
  中图分类号： TN912.34
- 网络出版日期：2015-09，
  
  纸质出版日期：2015-09-25
- 稿件说明：
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屈丹, 张文林. 基于稀疏组LASSO约束的本征音子说话人自适应[J]. 通信学报, 2015,36(9):47-54.

Dan QU, Wen-lin ZHANG. Sparse group LASSO constraint eigenphone speaker adaptation method for speech recognition[J]. Journal on communications, 2015, 36(9): 47-54.
屈丹, 张文林. 基于稀疏组LASSO约束的本征音子说话人自适应[J]. 通信学报, 2015,36(9):47-54. DOI： 10.11959/j.issn.1000-436x.2015241.

Dan QU, Wen-lin ZHANG. Sparse group LASSO constraint eigenphone speaker adaptation method for speech recognition[J]. Journal on communications, 2015, 36(9): 47-54. DOI： 10.11959/j.issn.1000-436x.2015241.

摘要

本征音子说话人自适应方法在自适应数据量不足时会出现严重的过拟合现象，提出了一种基于稀疏组LASSO 约束的本征音子说话人自适应算法。首先给出隐马尔可夫—高斯混合模型下本征音子说话人自适应的基本原理；然后将稀疏组 LASSO 正则化引入到本征音子说话人自适应，通过调整权重因子控制模型的复杂度，并通过一种加速近点梯度的数学优化算法来实现；最后将稀疏组 LASSO 约束的自适应算法与当前多种正则化约束的自适应方法进行比较。汉语连续语音识别的说话人自适应实验表明，引入稀疏组 LASSO 约束后，本征音子说话人自适应方法的性能得到了明显提高，且稀疏组LASSO约束方法优于l

、l

和弹性网正则化方法。

Abstract

Original eigenphone speaker adaptation method

performed well when the amount of adaptation data was suffi-cient.However

it suffered from server overfitting when insufficient amount of adaptation data was provided.A sparse group LASSO(SGL) constraint eigenphone speaker adaptation method was proposed.Firstly

the principle of eigenphone speaker adaptation was introduced in case of hidden Markov model-Gaussian mixture model (HMM-GMM) based speech recognition system.Then

a sparse group LASSO was applied to estimation of the eigenphone matrix.The weight of the SGL norm was adjusted to control the complexity of the adaptation model.Finally

an accelerated proximal gradient method was adopted to solve the mathematic optimization.The method was compared with up-to-date norm algorithms.Experiments on an mandarin Chinese continuous speech recognition task show that

the performance of the SGL con-straint eigenphone method can improve remarkably the performance of the system than original eigenphone method

and is also superior to l

、l

-norm and elastic net constraint methods.

关键词

Keywords

references

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